Suggesting executable actions in response to detecting events

ABSTRACT

Systems and processes for providing, via an electronic device, suggested user actions. The suggested actions are provided in response to detecting an occurrence of a predefined event occurring in the user&#39;s day. The occurrence of the anchor is encoded in signals generated by the electronic device. The occurrence of the anchor is detectable via monitoring and analysis of electronic signals. Based on the user&#39;s previous interactions with the device, the occurrence of the anchor is indicative of user behavior and/or action taken in response to the anchor. Machine learning (ML) is employed to train an anchor model to associate actions taken in response to anchor occurrences. The trained anchor model is employed to detect anchors and provide suggested actions in response to the detected anchor occurrence. The suggested action is based on a type of anchor occurrence and contextual conditions of the anchor occurrences.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.63/033,110, filed Jun. 1, 2020, entitled “SUGGESTING EXECUTABLE ACTIONSIN RESPONSE TO DETECTING EVENTS.” The entire contents of each of theseapplications are hereby incorporated by reference.

FIELD

This relates generally to intelligent automated assistants and, morespecifically, to enable digital assistants in intelligently suggestingexecutable actions, in response to detecting events that indicate userbehavior, based on previous user interactions with the electronic deviceimplementing the digital assistant.

BACKGROUND

Intelligent automated assistants (or digital assistants) can provide abeneficial interface between human users and electronic devices. Suchassistants can allow users to interact with devices or systems usingnatural language in spoken and/or text forms. For example, a user canprovide a speech input containing a user request to a digital assistantoperating on an electronic device. The digital assistant can interpretthe user's intent from the speech input and operationalize the user'sintent into tasks. The tasks can then be performed by executing one ormore services of the electronic device, and a relevant output responsiveto the user request can be returned to the user.

SUMMARY

Example methods are disclosed herein. One example method includestraining a predictive model (e.g., an anchor model) for a detected event(e.g., an anchor) that indicates a behavior of a user of an electronicdevice enabled to execute each action type of a set of enabled actiontypes. The electronic device may have one or more processors and amemory. The electronic device may perform one or more actions and oroperations. The method may include, at the electronic device and basedon a plurality of signals generated by the electronic device, detectinga plurality of training actions and a plurality of event occurrences ofthe event. Each of the plurality of event occurrences may be associatedwith metadata indicating a contextual condition of the event occurrence.Each of the plurality of training actions may be initiated via the userinteracting with the electronic device and is classified as an actiontype of the set of enabled action types. A set of candidate action typesmay be determined. Determining the set of candidate action types may bebased on a plurality of correlations between each of the plurality oftraining actions and each of the plurality of event occurrences. The setof candidate action types may be a subset of the set of enabled actiontypes. A ranking for each action type of the set of candidate actiontypes may be determined. Determining the ranking for an action type maybe based on a portion of the plurality of training actions that areclassified as the action type. Determining the ranking of the actiontype may be further based on the one or more contextual conditionsindicated by the metadata associated with a portion of the plurality ofevents occurrences that are correlated, via the plurality ofcorrelations, with the portion of the plurality of training actions. Insome embodiments, a first action type of the set of candidate actiontypes may be selected based on the determined ranking for each actiontype of the set of candidate action types. A first portion of theplurality of training actions and a first portion of the plurality ofevent occurrences may be selected. Each of the first portion of theplurality of training actions may be classified as the first actiontype. Each of the first portion of the plurality of event occurrencesmay be correlated, via the plurality of correlations, with at least oneof the first portion of the plurality of training actions. A temporaloffset for the first action type may be determined based on a temporaldistribution of the first of the plurality of training actions, withrespect to the first portion of the plurality of event occurrences. Insome embodiments, the predictive model may be updated to generate, inresponse to another occurrence of the event, a suggested action. Theprovided suggested action is in accordance with the first action typeand the temporal offset of the first action type.

Example non-transitory computer-readable media are disclosed herein. Anexample non-transitory computer-readable storage medium stores one ormore programs. The one or more programs comprise instructions, whichwhen executed by one or more processors of an electronic device, causethe electronic device to perform actions for training a predictive modelfor an event that indicates a behavior user of an electronic deviceenabled to execute each action type of a set of enabled action types.The actions may include, at the electronic device and based on aplurality of signals generated by the electronic device, detecting aplurality of training actions and a plurality of event occurrences ofthe event. Each of the plurality of event occurrences may be associatedwith metadata indicating a contextual condition of the event occurrence.Each of the plurality of training actions may be initiated via the userinteracting with the electronic device and is classified as an actiontype of the set of enabled action types. A set of candidate action typesmay be determined. Determining the set of candidate action types may bebased on a plurality of correlations between each of the plurality oftraining actions and each of the plurality of event occurrences. The setof candidate action types may be a subset of the set of enabled actiontypes. A ranking for each action type of the set of candidate actiontypes may be determined. Determining the ranking for an action type maybe based on a portion of the plurality of training actions that areclassified as the action type. Determining the ranking of the actiontype may be further based on the one or more contextual conditionsindicated by the metadata associated with a portion of the plurality ofevents occurrences that are correlated, via the plurality ofcorrelations, with the portion of the plurality of training actions. Insome embodiments, a first action type of the set of candidate actiontypes may be selected based on the determined ranking for each actiontype of the set of candidate action types. A first portion of theplurality of training actions and a first portion of the plurality ofevent occurrences may be selected. Each of the first portion of theplurality of training actions may be classified as the first actiontype. Each of the first portion of the plurality of event occurrencesmay be correlated, via the plurality of correlations, with at least oneof the first portion of the plurality of training actions. A temporaloffset for the first action type may be determined based on a temporaldistribution of the first of the plurality of training actions, withrespect to the first portion of the plurality of event occurrences. Insome embodiments, the predictive model may be updated to generate, inresponse to another occurrence of the event, a suggested action. Theprovided suggested action is in accordance with the first action typeand the temporal offset of the first action type.

Example electronic devices are disclosed herein. An example electronicdevice comprises one or more processors; a memory; and one or moreprograms, where the one or more programs are stored in the memory andconfigured to be executed by the one or more processors, the one or moreprograms including instructions for performing operations for training apredictive model for an event that indicates a behavior user of anelectronic device enabled to execute each action type of a set ofenabled action types. The operations may include, at the electronicdevice and based on a plurality of signals generated by the electronicdevice, detecting a plurality of training actions and a plurality ofevent occurrences of the event. Each of the plurality of eventoccurrences may be associated with metadata indicating a contextualcondition of the event occurrence. Each of the plurality of trainingactions may be initiated via the user interacting with the electronicdevice and is classified as an action type of the set of enabled actiontypes. A set of candidate action types may be determined. Determiningthe set of candidate action types may be based on a plurality ofcorrelations between each of the plurality of training actions and eachof the plurality of event occurrences. The set of candidate action typesmay be a subset of the set of enabled action types. A ranking for eachaction type of the set of candidate action types may be determined.Determining the ranking for an action type may be based on a portion ofthe plurality of training actions that are classified as the actiontype. Determining the ranking of the action type may be further based onthe one or more contextual conditions indicated by the metadataassociated with a portion of the plurality of events occurrences thatare correlated, via the plurality of correlations, with the portion ofthe plurality of training actions. In some embodiments, a first actiontype of the set of candidate action types may be selected based on thedetermined ranking for each action type of the set of candidate actiontypes. A first portion of the plurality of training actions and a firstportion of the plurality of event occurrences may be selected. Each ofthe first portion of the plurality of training actions may be classifiedas the first action type. Each of the first portion of the plurality ofevent occurrences may be correlated, via the plurality of correlations,with at least one of the first portion of the plurality of trainingactions. A temporal offset for the first action type may be determinedbased on a temporal distribution of the first of the plurality oftraining actions, with respect to the first portion of the plurality ofevent occurrences. In some embodiments, the predictive model may beupdated to generate, in response to another occurrence of the event, asuggested action. The provided suggested action is in accordance withthe first action type and the temporal offset of the first action type.

Another example method may be for employing a predictive model for anevent that indicates a behavior of a user of an electronic device. Theother method may include, based on one or more signals generated by theelectronic device, detecting an event occurrence of the event. The eventoccurrence may be associated with metadata indicating a contextualcondition of the event occurrence. In accordance with the eventoccurrence, a suggested action and a temporal offset may be receivedfrom the predictive model. The suggested action may be provided to theuser within the temporal offset from the event occurrence.

Another example non-transitory computer-readable storage medium storesone or more other programs. The one or more other programs compriseinstructions, which when executed by one or more processors of anelectronic device, cause the electronic device to perform otheroperations for employing a predictive model for an event that indicatesa behavior of a user of the electronic device. The other operations mayinclude based on one or more signals generated by the electronic device,detecting an event occurrence of the event. The event occurrence may beassociated with metadata indicating a contextual condition of the eventoccurrence. In accordance with the event occurrence, a suggested actionand a temporal offset may be received from the predictive model. Thesuggested action may be provided to the user within the temporal offsetfrom the event occurrence.

Another example electronic device comprises one or more processors, amemory; and one or more other programs, where the one or more otherprograms are stored in the memory and configured to be executed by theone or more processors, the one or more programs including instructionsfor performing other operations for employing a predictive model for anevent that indicates a behavior of a user of the electronic device. Theother operations may include, based on one or more signals generated bythe electronic device, detecting an event occurrence of the event. Theevent occurrence may be associated with metadata indicating a contextualcondition of the event occurrence. In accordance with the eventoccurrence, a suggested action and a temporal offset may be receivedfrom the predictive model. The suggested action may be provided to theuser within the temporal offset from the event occurrence.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a system and environment forimplementing a digital assistant, according to various examples.

FIG. 2A is a block diagram illustrating a portable multifunction deviceimplementing the client-side portion of a digital assistant, accordingto various examples.

FIG. 2B is a block diagram illustrating exemplary components for eventhandling, according to various examples.

FIG. 3 illustrates a portable multifunction device implementing theclient-side portion of a digital assistant, according to variousexamples.

FIG. 4 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface, according to various examples.

FIG. 5A illustrates an exemplary user interface for a menu ofapplications on a portable multifunction device, according to variousexamples.

FIG. 5B illustrates an exemplary user interface for a multifunctiondevice with a touch-sensitive surface that is separate from the display,according to various examples.

FIG. 6A illustrates a personal electronic device, according to variousexamples.

FIG. 6B is a block diagram illustrating a personal electronic device,according to various examples.

FIG. 7A is a block diagram illustrating a digital assistant system or aserver portion thereof, according to various examples.

FIG. 7B illustrates the functions of the digital assistant shown in FIG.7A, according to various examples.

FIG. 7C illustrates a portion of an ontology, according to variousexamples.

FIG. 8A illustrates a classification decision tree for the action typeof playing a particular podcast and the anchor type of the user wakeningup, according to various examples.

FIG. 8B illustrates a classification decision tree for the action typeof playing a particular song and the anchor type of the user wakeningup, according to various examples.

FIG. 9 illustrates a histogram for the temporal distribution of theaction type of playing a particular podcast, in conjunction with awaking up anchor, according to various examples.

FIG. 10 illustrates process for training a predictive model that enablesproviding suggested actions in response to an occurrence of an anchorevent, according to various examples.

FIG. 11 illustrates process 1100 for deploying a trained predictivemodel at an electronic device, according to various examples.

DETAILED DESCRIPTION

In the following description of examples, reference is made to theaccompanying drawings in which are shown by way of illustration specificexamples that can be practiced. It is to be understood that otherexamples can be used and structural changes can be made withoutdeparting from the scope of the various examples.

The embodiments are directed to providing, via an electronic device,suggested user actions. The suggested actions may be provided inresponse to detecting an occurrence of an anchor event. An anchor event(or simply an anchor) may be a predefined event occurring in the user'sday. For example, upon waking up in the morning (e.g., a user waking upmay be an example of an anchor), an electronic device may provide anotification that includes a suggestion for playing the newest episodeof a particular podcast (i.e., a suggested user action), where the userfrequently listens to the particular podcast upon waking up in themorning. The occurrence of the anchor (or event) may be detected via oneor more signals generated by the electronic device (e.g., the device'salarm clock function executing, the device being transitioned from a “DoNot Disturb Mode” to an “Active Mode,” or the like). An occurrence ofthe anchor may be detectable via the monitoring and analysis ofelectronic signals generated by the electronic device. Based on theuser's previous interactions with the device (e.g., encoded in trainingdata), the occurrence of the anchor may be indicative of user behaviorand/or action taken in response to the event in the user's day. By wayof non-limiting examples, an anchor may include the user entering alocation of interest (LOI), such as but not limited to the user's home,office, a fitness center, airport, shopping center, or the like. Becausean anchor may be an event that indicates user behavior, an anchor may bereferred throughout as an anchor event, or simply an event. Theoccurrence of an anchor may be referred to as an anchor occurrence.

Other non-limiting examples of an occurrence of an anchor include theuser beginning to use an electronic device after an extended period ofdevice idle time, the user finishing a workout, the user waking up, theuser going to bed, the user pairing a Bluetooth-enabled device to theelectronic device, the user beginning or completing a calendar event,and the like. Still other examples of an anchor include the userlaunching a specific application and/or employing a specificfunctionality of the device. Any event detectable via signatures encodedin signals generated by the electronic device may be an anchor. Asignature (e.g., a pattern within the electronic signals generated bythe device) that indicates an occurrence of a specific anchor may bepre-determined, pre-computed, and/or pre-learned (e.g., via supervisedor unsupervised machine learning (ML) methods). Thus, the electronicdevice may monitor its various signals and detect an occurrence of thespecific anchor by identifying and/or detecting one or more signatureswithin the signals that indicates the specific anchor. For example, anoccurrence of a waking up anchor may be detected via signals of theelectronic device that indicates that the user has terminated, orotherwise ended, an “idle” state of the electronic device (or that thedevice's alarm went off). Upon detecting an occurrence of the anchor,the various embodiments may suggest one or more user actions thatcorrelate (via previously generated training data) with the occurrenceof the anchor.

A user action (i.e., an action) may include an invocation, execution, orotherwise launching a specific application, capability, functionality,or command that is enabled via the electronic device. By way ofnon-limiting examples, a user action may include but is not limited toplaying a specific audio/video content (e.g., a podcast, a musicplaylist, an audio book, a lecture, a television series, a movie, or thelike), launching an application installed on the device (e.g., a workoutapplication, a meditation application, a ride-share application, a fooddelivery application, a social network application, or the like),sending an electronic communication (e.g., email, SMS, tweet, or thelike) to another user, user group, or social network, turning on/offfunctionality of the device (e.g., turning off/on an Airplane mode ofthe device), updating various settings and/or configurations of thedevice, creating a calendar event, or the like.

When providing a suggested action, the various embodiments may provide anotification (e.g., a “pop-up” notification) that indicates thesuggested action. The notification may be interactive, in that the usermay initiate the action via an interactive selection of thenotification. Upon selecting the notification, the suggested action maybe executed and/or the execution of the action may be initiated, by thedevice.

In the various embodiments, one or more machine learning (ML) models maybe trained to learn various statistical correlations (or associations)between occurrences of a specific anchor and actions (e.g., innovationsof device functionalities or capabilities) initiated by the user. Any ofthe various ML models employed by the various embodiments may be hereincollectively referred to as “anchor models,” because the models aretrained to detect occurrences of various anchors and suggest one or moreactions that the user is statistically likely to be initiate in responseto the occurrence (or impending occurrence) of the anchor. Thus, thevarious embodiments include the training of anchor models, as well asemploying the trained anchor models to enhance the user's experience(UX) of employing the device. Supervised or unsupervised ML may beemployed to train the models to determine the context (e.g., contextualfeatures) of an anchor occurrence, and base, tailor, or selectivelytarget the suggested actions on the context of the anchor

Although the following description uses terms “first,” “second,” etc. todescribe various elements, these elements should not be limited by theterms. These terms are only used to distinguish one element fromanother. For example, a first input could be termed a second input, and,similarly, a second input could be termed a first input, withoutdeparting from the scope of the various described examples. The firstinput and the second input are both inputs and, in some cases, areseparate and different inputs.

The terminology used in the description of the various describedexamples herein is for the purpose of describing particular examplesonly and is not intended to be limiting. As used in the description ofthe various described examples and the appended claims, the singularforms “a,” “an,” and “the” are intended to include the plural forms aswell, unless the context clearly indicates otherwise. It will also beunderstood that the term “and/or” as used herein refers to andencompasses any and all possible combinations of one or more of theassociated listed items. It will be further understood that the terms“includes,” “including,” “comprises,” and/or “comprising,” when used inthis specification, specify the presence of stated features, integers,steps, operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

The term “if” may be construed to mean “when” or “upon” or “in responseto determining” or “in response to detecting,” depending on the context.Similarly, the phrase “if it is determined” or “if [a stated conditionor event] is detected” may be construed to mean “upon determining” or“in response to determining” or “upon detecting [the stated condition orevent]” or “in response to detecting [the stated condition or event],”depending on the context.

1. System and Environment

FIG. 1 illustrates a block diagram of system 100 according to variousexamples. In some examples, system 100 implements a digital assistant.The terms “digital assistant,” “virtual assistant,” “intelligentautomated assistant,” or “automatic digital assistant” refer to anyinformation processing system that interprets natural language input inspoken and/or textual form to infer user intent, and performs actionsbased on the inferred user intent. For example, to act on an inferreduser intent, the system performs one or more of the following:identifying a task flow with steps and parameters designed to accomplishthe inferred user intent, inputting specific requirements from theinferred user intent into the task flow; executing the task flow byinvoking programs, methods, services, APIs, or the like; and generatingoutput responses to the user in an audible (e.g., speech) and/or visualform.

Specifically, a digital assistant is capable of accepting a user requestat least partially in the form of a natural language command, request,statement, narrative, and/or inquiry. Typically, the user request seekseither an informational answer or performance of a task by the digitalassistant. A satisfactory response to the user request includes aprovision of the requested informational answer, a performance of therequested task, or a combination of the two. For example, a user asksthe digital assistant a question, such as “Where am I right now?” Basedon the user's current location, the digital assistant answers, “You arein Central Park near the west gate.” The user also requests theperformance of a task, for example, “Please invite my friends to mygirlfriend's birthday party next week.” In response, the digitalassistant can acknowledge the request by saying “Yes, right away,” andthen send a suitable calendar invite on behalf of the user to each ofthe user's friends listed in the user's electronic address book. Duringperformance of a requested task, the digital assistant sometimesinteracts with the user in a continuous dialogue involving multipleexchanges of information over an extended period of time. There arenumerous other ways of interacting with a digital assistant to requestinformation or performance of various tasks. In addition to providingverbal responses and taking programmed actions, the digital assistantalso provides responses in other visual or audio forms, e.g., as text,alerts, music, videos, animations, etc.

As shown in FIG. 1, in some examples, a digital assistant is implementedaccording to a client-server model. The digital assistant includesclient-side portion 102 (hereafter “DA client 102”) executed on userdevice 104 and server-side portion 106 (hereafter “DA server 106”)executed on server system 108. DA client 102 communicates with DA server106 through one or more networks 110. DA client 102 provides client-sidefunctionalities such as user-facing input and output processing andcommunication with DA server 106. DA server 106 provides server-sidefunctionalities for any number of DA clients 102 each residing on arespective user device 104.

In some examples, DA server 106 includes client-facing I/O interface112, one or more processing modules 114, data and models 116, and I/Ointerface to external services 118. The client-facing I/O interface 112facilitates the client-facing input and output processing for DA server106. One or more processing modules 114 utilize data and models 116 toprocess speech input and determine the user's intent based on naturallanguage input. Further, one or more processing modules 114 perform taskexecution based on inferred user intent. In some examples, DA server 106communicates with external services 120 through network(s) 110 for taskcompletion or information acquisition. I/O interface to externalservices 118 facilitates such communications.

User device 104 can be any suitable electronic device. In some examples,user device 104 is a portable multifunctional device (e.g., device 200,described below with reference to FIG. 2A), a multifunctional device(e.g., device 400, described below with reference to FIG. 4), or apersonal electronic device (e.g., device 600, described below withreference to FIGS. 6A-6B.) A portable multifunctional device is, forexample, a mobile telephone that also contains other functions, such asPDA and/or music player functions. Specific examples of portablemultifunction devices include the Apple Watch®, iPhone®, iPod Touch®,and iPad® devices from Apple Inc. of Cupertino, Calif. Other examples ofportable multifunction devices include, without limitation,earphones/headphones, speakers, and laptop or tablet computers. Further,in some examples, user device 104 is a non-portable multifunctionaldevice. In particular, user device 104 is a desktop computer, a gameconsole, a speaker, a television, or a television set-top box. In someexamples, user device 104 includes a touch-sensitive surface (e.g.,touch screen displays and/or touchpads). Further, user device 104optionally includes one or more other physical user-interface devices,such as a physical keyboard, a mouse, and/or a joystick. Variousexamples of electronic devices, such as multifunctional devices, aredescribed below in greater detail.

Examples of communication network(s) 110 include local area networks(LAN) and wide area networks (WAN), e.g., the Internet. Communicationnetwork(s) 110 is implemented using any known network protocol,including various wired or wireless protocols, such as, for example,Ethernet, Universal Serial Bus (USB), FIREWIRE, Global System for MobileCommunications (GSM), Enhanced Data GSM Environment (EDGE), codedivision multiple access (CDMA), time division multiple access (TDMA),Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP), Wi-MAX, or anyother suitable communication protocol.

Server system 108 is implemented on one or more standalone dataprocessing apparatus or a distributed network of computers. In someexamples, server system 108 also employs various virtual devices and/orservices of third-party service providers (e.g., third-party cloudservice providers) to provide the underlying computing resources and/orinfrastructure resources of server system 108.

In some examples, user device 104 communicates with DA server 106 viasecond user device 122. Second user device 122 is similar or identicalto user device 104. For example, second user device 122 is similar todevices 200, 400, or 600 described below with reference to FIGS. 2A, 4,and 6A-6B. User device 104 is configured to communicatively couple tosecond user device 122 via a direct communication connection, such asBluetooth, NFC, BTLE, or the like, or via a wired or wireless network,such as a local Wi-Fi network. In some examples, second user device 122is configured to act as a proxy between user device 104 and DA server106. For example, DA client 102 of user device 104 is configured totransmit information (e.g., a user request received at user device 104)to DA server 106 via second user device 122. DA server 106 processes theinformation and returns relevant data (e.g., data content responsive tothe user request) to user device 104 via second user device 122.

In some examples, user device 104 is configured to communicateabbreviated requests for data to second user device 122 to reduce theamount of information transmitted from user device 104. Second userdevice 122 is configured to determine supplemental information to add tothe abbreviated request to generate a complete request to transmit to DAserver 106. This system architecture can advantageously allow userdevice 104 having limited communication capabilities and/or limitedbattery power (e.g., a watch or a similar compact electronic device) toaccess services provided by DA server 106 by using second user device122, having greater communication capabilities and/or battery power(e.g., a mobile phone, laptop computer, tablet computer, or the like),as a proxy to DA server 106. While only two user devices 104 and 122 areshown in FIG. 1, it should be appreciated that system 100, in someexamples, includes any number and type of user devices configured inthis proxy configuration to communicate with DA server system 106.

Although the digital assistant shown in FIG. 1 includes both aclient-side portion (e.g., DA client 102) and a server-side portion(e.g., DA server 106), in some examples, the functions of a digitalassistant are implemented as a standalone application installed on auser device. In addition, the divisions of functionalities between theclient and server portions of the digital assistant can vary indifferent implementations. For instance, in some examples, the DA clientis a thin-client that provides only user-facing input and outputprocessing functions, and delegates all other functionalities of thedigital assistant to a backend server.

2. Electronic Devices

Attention is now directed toward embodiments of electronic devices forimplementing the client-side portion of a digital assistant. FIG. 2A isa block diagram illustrating portable multifunction device 200 withtouch-sensitive display system 212 in accordance with some embodiments.Touch-sensitive display 212 is sometimes called a “touch screen” forconvenience and is sometimes known as or called a “touch-sensitivedisplay system.” Device 200 includes memory 202 (which optionallyincludes one or more computer-readable storage mediums), memorycontroller 222, one or more processing units (CPUs) 220, peripheralsinterface 218, RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, input/output (I/O) subsystem 206, other input controldevices 216, and external port 224. Device 200 optionally includes oneor more optical sensors 264. Device 200 optionally includes one or morecontact intensity sensors 265 for detecting intensity of contacts ondevice 200 (e.g., a touch-sensitive surface such as touch-sensitivedisplay system 212 of device 200). Device 200 optionally includes one ormore tactile output generators 267 for generating tactile outputs ondevice 200 (e.g., generating tactile outputs on a touch-sensitivesurface such as touch-sensitive display system 212 of device 200 ortouchpad 455 of device 400). These components optionally communicateover one or more communication buses or signal lines 203.

As used in the specification and claims, the term “intensity” of acontact on a touch-sensitive surface refers to the force or pressure(force per unit area) of a contact (e.g., a finger contact) on thetouch-sensitive surface, or to a substitute (proxy) for the force orpressure of a contact on the touch-sensitive surface. The intensity of acontact has a range of values that includes at least four distinctvalues and more typically includes hundreds of distinct values (e.g., atleast 256). Intensity of a contact is, optionally, determined (ormeasured) using various approaches and various sensors or combinationsof sensors. For example, one or more force sensors underneath oradjacent to the touch-sensitive surface are, optionally, used to measureforce at various points on the touch-sensitive surface. In someimplementations, force measurements from multiple force sensors arecombined (e.g., a weighted average) to determine an estimated force of acontact. Similarly, a pressure-sensitive tip of a stylus is, optionally,used to determine a pressure of the stylus on the touch-sensitivesurface. Alternatively, the size of the contact area detected on thetouch-sensitive surface and/or changes thereto, the capacitance of thetouch-sensitive surface proximate to the contact and/or changes thereto,and/or the resistance of the touch-sensitive surface proximate to thecontact and/or changes thereto are, optionally, used as a substitute forthe force or pressure of the contact on the touch-sensitive surface. Insome implementations, the substitute measurements for contact force orpressure are used directly to determine whether an intensity thresholdhas been exceeded (e.g., the intensity threshold is described in unitscorresponding to the substitute measurements). In some implementations,the substitute measurements for contact force or pressure are convertedto an estimated force or pressure, and the estimated force or pressureis used to determine whether an intensity threshold has been exceeded(e.g., the intensity threshold is a pressure threshold measured in unitsof pressure). Using the intensity of a contact as an attribute of a userinput allows for user access to additional device functionality that mayotherwise not be accessible by the user on a reduced-size device withlimited real estate for displaying affordances (e.g., on atouch-sensitive display) and/or receiving user input (e.g., via atouch-sensitive display, a touch-sensitive surface, or aphysical/mechanical control such as a knob or a button).

As used in the specification and claims, the term “tactile output”refers to physical displacement of a device relative to a previousposition of the device, physical displacement of a component (e.g., atouch-sensitive surface) of a device relative to another component(e.g., housing) of the device, or displacement of the component relativeto a center of mass of the device that will be detected by a user withthe user's sense of touch. For example, in situations where the deviceor the component of the device is in contact with a surface of a userthat is sensitive to touch (e.g., a finger, palm, or other part of auser's hand), the tactile output generated by the physical displacementwill be interpreted by the user as a tactile sensation corresponding toa perceived change in physical characteristics of the device or thecomponent of the device. For example, movement of a touch-sensitivesurface (e.g., a touch-sensitive display or trackpad) is, optionally,interpreted by the user as a “down click” or “up click” of a physicalactuator button. In some cases, a user will feel a tactile sensationsuch as an “down click” or “up click” even when there is no movement ofa physical actuator button associated with the touch-sensitive surfacethat is physically pressed (e.g., displaced) by the user's movements. Asanother example, movement of the touch-sensitive surface is, optionally,interpreted or sensed by the user as “roughness” of the touch-sensitivesurface, even when there is no change in smoothness of thetouch-sensitive surface. While such interpretations of touch by a userwill be subject to the individualized sensory perceptions of the user,there are many sensory perceptions of touch that are common to a largemajority of users. Thus, when a tactile output is described ascorresponding to a particular sensory perception of a user (e.g., an “upclick,” a “down click,” “roughness”), unless otherwise stated, thegenerated tactile output corresponds to physical displacement of thedevice or a component thereof that will generate the described sensoryperception for a typical (or average) user.

It should be appreciated that device 200 is only one example of aportable multifunction device, and that device 200 optionally has moreor fewer components than shown, optionally combines two or morecomponents, or optionally has a different configuration or arrangementof the components. The various components shown in FIG. 2A areimplemented in hardware, software, or a combination of both hardware andsoftware, including one or more signal processing and/orapplication-specific integrated circuits.

Memory 202 includes one or more computer-readable storage mediums. Thecomputer-readable storage mediums are, for example, tangible andnon-transitory. Memory 202 includes high-speed random access memory andalso includes non-volatile memory, such as one or more magnetic diskstorage devices, flash memory devices, or other non-volatile solid-statememory devices. Memory controller 222 controls access to memory 202 byother components of device 200.

In some examples, a non-transitory computer-readable storage medium ofmemory 202 is used to store instructions (e.g., for performing aspectsof processes described below) for use by or in connection with aninstruction execution system, apparatus, or device, such as acomputer-based system, processor-containing system, or other system thatcan fetch the instructions from the instruction execution system,apparatus, or device and execute the instructions. In other examples,the instructions (e.g., for performing aspects of the processesdescribed below) are stored on a non-transitory computer-readablestorage medium (not shown) of the server system 108 or are dividedbetween the non-transitory computer-readable storage medium of memory202 and the non-transitory computer-readable storage medium of serversystem 108.

Peripherals interface 218 is used to couple input and output peripheralsof the device to CPU 220 and memory 202. The one or more processors 220run or execute various software programs and/or sets of instructionsstored in memory 202 to perform various functions for device 200 and toprocess data. In some embodiments, peripherals interface 218, CPU 220,and memory controller 222 are implemented on a single chip, such as chip204. In some other embodiments, they are implemented on separate chips.

RF (radio frequency) circuitry 208 receives and sends RF signals, alsocalled electromagnetic signals. RF circuitry 208 converts electricalsignals to/from electromagnetic signals and communicates withcommunications networks and other communications devices via theelectromagnetic signals. RF circuitry 208 optionally includes well-knowncircuitry for performing these functions, including but not limited toan antenna system, an RF transceiver, one or more amplifiers, a tuner,one or more oscillators, a digital signal processor, a CODEC chipset, asubscriber identity module (SIM) card, memory, and so forth. RFcircuitry 208 optionally communicates with networks, such as theInternet, also referred to as the World Wide Web (WWW), an intranetand/or a wireless network, such as a cellular telephone network, awireless local area network (LAN) and/or a metropolitan area network(MAN), and other devices by wireless communication. The RF circuitry 208optionally includes well-known circuitry for detecting near fieldcommunication (NFC) fields, such as by a short-range communicationradio. The wireless communication optionally uses any of a plurality ofcommunications standards, protocols, and technologies, including but notlimited to Global System for Mobile Communications (GSM), Enhanced DataGSM Environment (EDGE), high-speed downlink packet access (HSDPA),high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO),HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), nearfield communication (NFC), wideband code division multiple access(W-CDMA), code division multiple access (CDMA), time division multipleaccess (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity(Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n,and/or IEEE 802.11ac), voice over Internet Protocol (VoIP), Wi-MAX, aprotocol for e mail (e.g., Internet message access protocol (IMAP)and/or post office protocol (POP)), instant messaging (e.g., extensiblemessaging and presence protocol (XMPP), Session Initiation Protocol forInstant Messaging and Presence Leveraging Extensions (SIMPLE), InstantMessaging and Presence Service (IMPS)), and/or Short Message Service(SMS), or any other suitable communication protocol, includingcommunication protocols not yet developed as of the filing date of thisdocument.

Audio circuitry 210, speaker 211, and microphone 213 provide an audiointerface between a user and device 200. Audio circuitry 210 receivesaudio data from peripherals interface 218, converts the audio data to anelectrical signal, and transmits the electrical signal to speaker 211.Speaker 211 converts the electrical signal to human-audible sound waves.Audio circuitry 210 also receives electrical signals converted bymicrophone 213 from sound waves. Audio circuitry 210 converts theelectrical signal to audio data and transmits the audio data toperipherals interface 218 for processing. Audio data are retrieved fromand/or transmitted to memory 202 and/or RF circuitry 208 by peripheralsinterface 218. In some embodiments, audio circuitry 210 also includes aheadset jack (e.g., 312, FIG. 3). The headset jack provides an interfacebetween audio circuitry 210 and removable audio input/outputperipherals, such as output-only headphones or a headset with bothoutput (e.g., a headphone for one or both ears) and input (e.g., amicrophone).

I/O subsystem 206 couples input/output peripherals on device 200, suchas touch screen 212 and other input control devices 216, to peripheralsinterface 218. I/O subsystem 206 optionally includes display controller256, optical sensor controller 258, intensity sensor controller 259,haptic feedback controller 261, and one or more input controllers 260for other input or control devices. The one or more input controllers260 receive/send electrical signals from/to other input control devices216. The other input control devices 216 optionally include physicalbuttons (e.g., push buttons, rocker buttons, etc.), dials, sliderswitches, joysticks, click wheels, and so forth. In some alternateembodiments, input controller(s) 260 are, optionally, coupled to any (ornone) of the following: a keyboard, an infrared port, a USB port, and apointer device such as a mouse. The one or more buttons (e.g., 308, FIG.3) optionally include an up/down button for volume control of speaker211 and/or microphone 213. The one or more buttons optionally include apush button (e.g., 306, FIG. 3).

A quick press of the push button disengages a lock of touch screen 212or begin a process that uses gestures on the touch screen to unlock thedevice, as described in U.S. patent application Ser. No. 11/322,549,“Unlocking a Device by Performing Gestures on an Unlock Image,” filedDec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated byreference in its entirety. A longer press of the push button (e.g., 306)turns power to device 200 on or off. The user is able to customize afunctionality of one or more of the buttons. Touch screen 212 is used toimplement virtual or soft buttons and one or more soft keyboards.

Touch-sensitive display 212 provides an input interface and an outputinterface between the device and a user. Display controller 256 receivesand/or sends electrical signals from/to touch screen 212. Touch screen212 displays visual output to the user. The visual output includesgraphics, text, icons, video, and any combination thereof (collectivelytermed “graphics”). In some embodiments, some or all of the visualoutput correspond to user-interface objects.

Touch screen 212 has a touch-sensitive surface, sensor, or set ofsensors that accepts input from the user based on haptic and/or tactilecontact. Touch screen 212 and display controller 256 (along with anyassociated modules and/or sets of instructions in memory 202) detectcontact (and any movement or breaking of the contact) on touch screen212 and convert the detected contact into interaction withuser-interface objects (e.g., one or more soft keys, icons, web pages,or images) that are displayed on touch screen 212. In an exemplaryembodiment, a point of contact between touch screen 212 and the usercorresponds to a finger of the user.

Touch screen 212 uses LCD (liquid crystal display) technology, LPD(light emitting polymer display) technology, or LED (light emittingdiode) technology, although other display technologies may be used inother embodiments. Touch screen 212 and display controller 256 detectcontact and any movement or breaking thereof using any of a plurality oftouch sensing technologies now known or later developed, including butnot limited to capacitive, resistive, infrared, and surface acousticwave technologies, as well as other proximity sensor arrays or otherelements for determining one or more points of contact with touch screen212. In an exemplary embodiment, projected mutual capacitance sensingtechnology is used, such as that found in the iPhone® and iPod Touch®from Apple Inc. of Cupertino, Calif.

A touch-sensitive display in some embodiments of touch screen 212 isanalogous to the multi-touch sensitive touchpads described in thefollowing U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat. No.6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932(Westerman), and/or U.S. Patent Publication 2002/0015024A1, each ofwhich is hereby incorporated by reference in its entirety. However,touch screen 212 displays visual output from device 200, whereastouch-sensitive touchpads do not provide visual output.

A touch-sensitive display in some embodiments of touch screen 212 is asdescribed in the following applications: (1) U.S. patent applicationSer. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2,2006; (2) U.S. patent application Ser. No. 10/840,862, “MultipointTouchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No.10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30,2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures ForTouch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patentapplication Ser. No. 11/038,590, “Mode-Based Graphical User InterfacesFor Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patentapplication Ser. No. 11/228,758, “Virtual Input Device Placement On ATouch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patentapplication Ser. No. 11/228,700, “Operation Of A Computer With A TouchScreen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser.No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen VirtualKeyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No.11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. Allof these applications are incorporated by reference herein in theirentirety.

Touch screen 212 has, for example, a video resolution in excess of 100dpi. In some embodiments, the touch screen has a video resolution ofapproximately 160 dpi. The user makes contact with touch screen 212using any suitable object or appendage, such as a stylus, a finger, andso forth. In some embodiments, the user interface is designed to workprimarily with finger-based contacts and gestures, which can be lessprecise than stylus-based input due to the larger area of contact of afinger on the touch screen. In some embodiments, the device translatesthe rough finger-based input into a precise pointer/cursor position orcommand for performing the actions desired by the user.

In some embodiments, in addition to the touch screen, device 200includes a touchpad (not shown) for activating or deactivatingparticular functions. In some embodiments, the touchpad is atouch-sensitive area of the device that, unlike the touch screen, doesnot display visual output. The touchpad is a touch-sensitive surfacethat is separate from touch screen 212 or an extension of thetouch-sensitive surface formed by the touch screen.

Device 200 also includes power system 262 for powering the variouscomponents. Power system 262 includes a power management system, one ormore power sources (e.g., battery, alternating current (AC)), arecharging system, a power failure detection circuit, a power converteror inverter, a power status indicator (e.g., a light-emitting diode(LED)) and any other components associated with the generation,management and distribution of power in portable devices.

Device 200 also includes one or more optical sensors 264. FIG. 2A showsan optical sensor coupled to optical sensor controller 258 in I/Osubsystem 206. Optical sensor 264 includes charge-coupled device (CCD)or complementary metal-oxide semiconductor (CMOS) phototransistors.Optical sensor 264 receives light from the environment, projectedthrough one or more lenses, and converts the light to data representingan image. In conjunction with imaging module 243 (also called a cameramodule), optical sensor 264 captures still images or video. In someembodiments, an optical sensor is located on the back of device 200,opposite touch screen display 212 on the front of the device so that thetouch screen display is used as a viewfinder for still and/or videoimage acquisition. In some embodiments, an optical sensor is located onthe front of the device so that the user's image is obtained for videoconferencing while the user views the other video conferenceparticipants on the touch screen display. In some embodiments, theposition of optical sensor 264 can be changed by the user (e.g., byrotating the lens and the sensor in the device housing) so that a singleoptical sensor 264 is used along with the touch screen display for bothvideo conferencing and still and/or video image acquisition.

Device 200 optionally also includes one or more contact intensitysensors 265. FIG. 2A shows a contact intensity sensor coupled tointensity sensor controller 259 in I/O subsystem 206. Contact intensitysensor 265 optionally includes one or more piezoresistive strain gauges,capacitive force sensors, electric force sensors, piezoelectric forcesensors, optical force sensors, capacitive touch-sensitive surfaces, orother intensity sensors (e.g., sensors used to measure the force (orpressure) of a contact on a touch-sensitive surface). Contact intensitysensor 265 receives contact intensity information (e.g., pressureinformation or a proxy for pressure information) from the environment.In some embodiments, at least one contact intensity sensor is collocatedwith, or proximate to, a touch-sensitive surface (e.g., touch-sensitivedisplay system 212). In some embodiments, at least one contact intensitysensor is located on the back of device 200, opposite touch screendisplay 212, which is located on the front of device 200.

Device 200 also includes one or more proximity sensors 266. FIG. 2Ashows proximity sensor 266 coupled to peripherals interface 218.Alternately, proximity sensor 266 is coupled to input controller 260 inI/O subsystem 206. Proximity sensor 266 is performed as described inU.S. patent application Ser. No. 11/241,839, “Proximity Detector InHandheld Device”; Ser. No. 11/240,788, “Proximity Detector In HandheldDevice”; Ser. No. 11/620,702, “Using Ambient Light Sensor To AugmentProximity Sensor Output”; Ser. No. 11/586,862, “Automated Response ToAnd Sensing Of User Activity In Portable Devices”; and Ser. No.11/638,251, “Methods And Systems For Automatic Configuration OfPeripherals,” which are hereby incorporated by reference in theirentirety. In some embodiments, the proximity sensor turns off anddisables touch screen 212 when the multifunction device is placed nearthe user's ear (e.g., when the user is making a phone call).

Device 200 optionally also includes one or more tactile outputgenerators 267. FIG. 2A shows a tactile output generator coupled tohaptic feedback controller 261 in I/O subsystem 206. Tactile outputgenerator 267 optionally includes one or more electroacoustic devicessuch as speakers or other audio components and/or electromechanicaldevices that convert energy into linear motion such as a motor,solenoid, electroactive polymer, piezoelectric actuator, electrostaticactuator, or other tactile output generating component (e.g., acomponent that converts electrical signals into tactile outputs on thedevice). Contact intensity sensor 265 receives tactile feedbackgeneration instructions from haptic feedback module 233 and generatestactile outputs on device 200 that are capable of being sensed by a userof device 200. In some embodiments, at least one tactile outputgenerator is collocated with, or proximate to, a touch-sensitive surface(e.g., touch-sensitive display system 212) and, optionally, generates atactile output by moving the touch-sensitive surface vertically (e.g.,in/out of a surface of device 200) or laterally (e.g., back and forth inthe same plane as a surface of device 200). In some embodiments, atleast one tactile output generator sensor is located on the back ofdevice 200, opposite touch screen display 212, which is located on thefront of device 200.

Device 200 also includes one or more accelerometers 268. FIG. 2A showsaccelerometer 268 coupled to peripherals interface 218. Alternately,accelerometer 268 is coupled to an input controller 260 in I/O subsystem206. Accelerometer 268 performs, for example, as described in U.S.Patent Publication No. 20050190059, “Acceleration-based Theft DetectionSystem for Portable Electronic Devices,” and U.S. Patent Publication No.20060017692, “Methods And Apparatuses For Operating A Portable DeviceBased On An Accelerometer,” both of which are incorporated by referenceherein in their entirety. In some embodiments, information is displayedon the touch screen display in a portrait view or a landscape view basedon an analysis of data received from the one or more accelerometers.Device 200 optionally includes, in addition to accelerometer(s) 268, amagnetometer (not shown) and a GPS (or GLONASS or other globalnavigation system) receiver (not shown) for obtaining informationconcerning the location and orientation (e.g., portrait or landscape) ofdevice 200.

In some embodiments, the software components stored in memory 202include operating system 226, communication module (or set ofinstructions) 228, contact/motion module (or set of instructions) 230,graphics module (or set of instructions) 232, text input module (or setof instructions) 234, Global Positioning System (GPS) module (or set ofinstructions) 235, Digital Assistant Client Module 229, and applications(or sets of instructions) 236. Further, memory 202 stores data andmodels, such as user data and models 231. Furthermore, in someembodiments, memory 202 (FIG. 2A) or 470 (FIG. 4) stores device/globalinternal state 257, as shown in FIGS. 2A and 4. Device/global internalstate 257 includes one or more of: active application state, indicatingwhich applications, if any, are currently active; display state,indicating what applications, views or other information occupy variousregions of touch screen display 212: sensor state, including informationobtained from the device's various sensors and input control devices216; and location information concerning the device's location and/orattitude.

Operating system 226 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS,WINDOWS, or an embedded operating system such as VxWorks) includesvarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communication between varioushardware and software components.

Communication module 228 facilitates communication with other devicesover one or more external ports 224 and also includes various softwarecomponents for handling data received by RF circuitry 208 and/orexternal port 224. External port 224 (e.g., Universal Serial Bus (USB),FIREWIRE, etc.) is adapted for coupling directly to other devices orindirectly over a network (e.g., the Internet, wireless LAN, etc.). Insome embodiments, the external port is a multi-pin (e.g., 30-pin)connector that is the same as, or similar to and/or compatible with, the30-pin connector used on iPod® (trademark of Apple Inc.) devices.

Contact/motion module 230 optionally detects contact with touch screen212 (in conjunction with display controller 256) and othertouch-sensitive devices (e.g., a touchpad or physical click wheel).Contact/motion module 230 includes various software components forperforming various operations related to detection of contact, such asdetermining if contact has occurred (e.g., detecting a finger-downevent), determining an intensity of the contact (e.g., the force orpressure of the contact or a substitute for the force or pressure of thecontact), determining if there is movement of the contact and trackingthe movement across the touch-sensitive surface (e.g., detecting one ormore finger-dragging events), and determining if the contact has ceased(e.g., detecting a finger-up event or a break in contact).Contact/motion module 230 receives contact data from the touch-sensitivesurface. Determining movement of the point of contact, which isrepresented by a series of contact data, optionally includes determiningspeed (magnitude), velocity (magnitude and direction), and/or anacceleration (a change in magnitude and/or direction) of the point ofcontact. These operations are, optionally, applied to single contacts(e.g., one finger contacts) or to multiple simultaneous contacts (e.g.,“multitouch”/multiple finger contacts). In some embodiments,contact/motion module 230 and display controller 256 detect contact on atouchpad.

In some embodiments, contact/motion module 230 uses a set of one or moreintensity thresholds to determine whether an operation has beenperformed by a user (e.g., to determine whether a user has “clicked” onan icon). In some embodiments, at least a subset of the intensitythresholds are determined in accordance with software parameters (e.g.,the intensity thresholds are not determined by the activation thresholdsof particular physical actuators and can be adjusted without changingthe physical hardware of device 200). For example, a mouse “click”threshold of a trackpad or touch screen display can be set to any of alarge range of predefined threshold values without changing the trackpador touch screen display hardware. Additionally, in some implementations,a user of the device is provided with software settings for adjustingone or more of the set of intensity thresholds (e.g., by adjustingindividual intensity thresholds and/or by adjusting a plurality ofintensity thresholds at once with a system-level click “intensity”parameter).

Contact/motion module 230 optionally detects a gesture input by a user.Different gestures on the touch-sensitive surface have different contactpatterns (e.g., different motions, timings, and/or intensities ofdetected contacts). Thus, a gesture is, optionally, detected bydetecting a particular contact pattern. For example, detecting a fingertap gesture includes detecting a finger-down event followed by detectinga finger-up (liftoff) event at the same position (or substantially thesame position) as the finger-down event (e.g., at the position of anicon). As another example, detecting a finger swipe gesture on thetouch-sensitive surface includes detecting a finger-down event followedby detecting one or more finger-dragging events, and subsequentlyfollowed by detecting a finger-up (liftoff) event.

Graphics module 232 includes various known software components forrendering and displaying graphics on touch screen 212 or other display,including components for changing the visual impact (e.g., brightness,transparency, saturation, contrast, or other visual property) ofgraphics that are displayed. As used herein, the term “graphics”includes any object that can be displayed to a user, including, withoutlimitation, text, web pages, icons (such as user-interface objectsincluding soft keys), digital images, videos, animations, and the like.

In some embodiments, graphics module 232 stores data representinggraphics to be used. Each graphic is, optionally, assigned acorresponding code. Graphics module 232 receives, from applicationsetc., one or more codes specifying graphics to be displayed along with,if necessary, coordinate data and other graphic property data, and thengenerates screen image data to output to display controller 256.

Haptic feedback module 233 includes various software components forgenerating instructions used by tactile output generator(s) 267 toproduce tactile outputs at one or more locations on device 200 inresponse to user interactions with device 200.

Text input module 234, which is, in some examples, a component ofgraphics module 232, provides soft keyboards for entering text invarious applications (e.g., contacts 237, email 240, IM 241, browser247, and any other application that needs text input).

GPS module 235 determines the location of the device and provides thisinformation for use in various applications (e.g., to telephone 238 foruse in location-based dialing; to camera 243 as picture/video metadata;and to applications that provide location-based services such as weatherwidgets, local yellow page widgets, and map/navigation widgets).

Digital assistant client module 229 includes various client-side digitalassistant instructions to provide the client-side functionalities of thedigital assistant. For example, digital assistant client module 229 iscapable of accepting voice input (e.g., speech input), text input, touchinput, and/or gestural input through various user interfaces (e.g.,microphone 213, accelerometer(s) 268, touch-sensitive display system212, optical sensor(s) 264, other input control devices 216, etc.) ofportable multifunction device 200. Digital assistant client module 229is also capable of providing output in audio (e.g., speech output),visual, and/or tactile forms through various output interfaces (e.g.,speaker 211, touch-sensitive display system 212, tactile outputgenerator(s) 267, etc.) of portable multifunction device 200. Forexample, output is provided as voice, sound, alerts, text messages,menus, graphics, videos, animations, vibrations, and/or combinations oftwo or more of the above. During operation, digital assistant clientmodule 229 communicates with DA server 106 using RF circuitry 208.

User data and models 231 include various data associated with the user(e.g., user-specific vocabulary data, user preference data,user-specified name pronunciations, data from the user's electronicaddress book, to-do lists, shopping lists, etc.) to provide theclient-side functionalities of the digital assistant. Further, user dataand models 231 include various models (e.g., speech recognition models,statistical language models, natural language processing models,ontology, task flow models, service models, etc.) for processing userinput and determining user intent.

In some examples, digital assistant client module 229 utilizes thevarious sensors, subsystems, and peripheral devices of portablemultifunction device 200 to gather additional information from thesurrounding environment of the portable multifunction device 200 toestablish a context associated with a user, the current userinteraction, and/or the current user input. In some examples, digitalassistant client module 229 provides the contextual information or asubset thereof with the user input to DA server 106 to help infer theuser's intent. In some examples, the digital assistant also uses thecontextual information to determine how to prepare and deliver outputsto the user. Contextual information is referred to as context data.

In some examples, the contextual information that accompanies the userinput includes sensor information, e.g., lighting, ambient noise,ambient temperature, images or videos of the surrounding environment,etc. In some examples, the contextual information can also include thephysical state of the device, e.g., device orientation, device location,device temperature, power level, speed, acceleration, motion patterns,cellular signals strength, etc. In some examples, information related tothe software state of DA server 106, e.g., running processes, installedprograms, past and present network activities, background services,error logs, resources usage, etc., and of portable multifunction device200 is provided to DA server 106 as contextual information associatedwith a user input.

In some examples, the digital assistant client module 229 selectivelyprovides information (e.g., user data 231) stored on the portablemultifunction device 200 in response to requests from DA server 106. Insome examples, digital assistant client module 229 also elicitsadditional input from the user via a natural language dialogue or otheruser interfaces upon request by DA server 106. Digital assistant clientmodule 229 passes the additional input to DA server 106 to help DAserver 106 in intent deduction and/or fulfillment of the user's intentexpressed in the user request.

A more detailed description of a digital assistant is described belowwith reference to FIGS. 7A-7C. It should be recognized that digitalassistant client module 229 can include any number of the sub-modules ofdigital assistant module 726 described below.

Applications 236 include the following modules (or sets ofinstructions), or a subset or superset thereof:

-   -   Contacts module 237 (sometimes called an address book or contact        list);    -   Telephone module 238;    -   Video conference module 239;    -   E-mail client module 240;    -   Instant messaging (IM) module 241;    -   Workout support module 242;    -   Camera module 243 for still and/or video images;    -   Image management module 244;    -   Video player module;    -   Music player module;    -   Browser module 247;    -   Calendar module 248;    -   Widget modules 249, which includes, in some examples, one or        more of; weather widget 249-1, stocks widget 249-2, calculator        widget 249-3, alarm clock widget 249-4, dictionary widget 249-5,        and other widgets obtained by the user, as well as user-created        widgets 249-6;    -   Widget creator module 250 for making user-created widgets 249-6;    -   Search module 251;    -   Video and music player module 252, which merges video player        module and music player module;    -   Notes module 253;    -   Map module 254; and/or    -   Online video module 255.

Examples of other applications 236 that are stored in memory 202 includeother word processing applications, other image editing applications,drawing applications, presentation applications, JAVA-enabledapplications, encryption, digital rights management, voice recognition,and voice replication.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, contacts module 237 are used to manage an address book or contactlist (e.g., stored in application internal state 292 of contacts module237 in memory 202 or memory 470), including: adding name(s) to theaddress book; deleting name(s) from the address book; associatingtelephone number(s), e-mail address(es), physical address(es) or otherinformation with a name; associating an image with a name; categorizingand sorting names; providing telephone numbers or e-mail addresses toinitiate and/or facilitate communications by telephone 238, videoconference module 239, e-mail 240, or IM 241; and so forth.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, touch screen 212, display controller 256, contact/motionmodule 230, graphics module 232, and text input module 234, telephonemodule 238 are used to enter a sequence of characters corresponding to atelephone number, access one or more telephone numbers in contactsmodule 237, modify a telephone number that has been entered, dial arespective telephone number, conduct a conversation, and disconnect orhang up when the conversation is completed. As noted above, the wirelesscommunication uses any of a plurality of communications standards,protocols, and technologies.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, touch screen 212, display controller 256, optical sensor264, optical sensor controller 258, contact/motion module 230, graphicsmodule 232, text input module 234, contacts module 237, and telephonemodule 238, video conference module 239 includes executable instructionsto initiate, conduct, and terminate a video conference between a userand one or more other participants in accordance with user instructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, e-mail client module 240 includes executableinstructions to create, send, receive, and manage e-mail in response touser instructions. In conjunction with image management module 244,e-mail client module 240 makes it very easy to create and send e-mailswith still or video images taken with camera module 243.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, the instant messaging module 241 includes executableinstructions to enter a sequence of characters corresponding to aninstant message, to modify previously entered characters, to transmit arespective instant message (for example, using a Short Message Service(SMS) or Multimedia Message Service (MMS) protocol for telephony-basedinstant messages or using XMPP, SIMPLE, or IMPS for Internet-basedinstant messages), to receive instant messages, and to view receivedinstant messages. In some embodiments, transmitted and/or receivedinstant messages include graphics, photos, audio files, video filesand/or other attachments as are supported in an MMS and/or an EnhancedMessaging Service (EMS). As used herein, “instant messaging” refers toboth telephony-based messages (e.g., messages sent using SMS or MMS) andInternet-based messages (e.g., messages sent using XMPP, SIMPLE, orIMPS).

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, GPS module 235, map module 254, and music playermodule, workout support module 242 includes executable instructions tocreate workouts (e.g., with time, distance, and/or calorie burninggoals); communicate with workout sensors (sports devices); receiveworkout sensor data; calibrate sensors used to monitor a workout; selectand play music for a workout: and display, store, and transmit workoutdata.

In conjunction with touch screen 212, display controller 256, opticalsensor(s) 264, optical sensor controller 258, contact/motion module 230,graphics module 232, and image management module 244, camera module 243includes executable instructions to capture still images or video(including a video stream) and store them into memory 202, modifycharacteristics of a still image or video, or delete a still image orvideo from memory 202.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, text input module 234,and camera module 243, image management module 244 includes executableinstructions to arrange, modify (e.g., edit), or otherwise manipulate,label, delete, present (e.g., in a digital slide show or album), andstore still and/or video images.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, browser module 247 includes executable instructions tobrowse the Internet in accordance with user instructions, includingsearching, linking to, receiving, and displaying web pages or portionsthereof, as well as attachments and other files linked to web pages.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, e-mail client module 240, and browser module 247,calendar module 248 includes executable instructions to create, display,modify, and store calendars and data associated with calendars (e.g.,calendar entries, to-do lists, etc.) in accordance with userinstructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, and browser module 247, widget modules 249 aremini-applications that can be downloaded and used by a user (e.g.,weather widget 249-1, stocks widget 249-2, calculator widget 249-3,alarm clock widget 249-4, and dictionary widget 249-5) or created by theuser (e.g., user-created widget 249-6). In some embodiments, a widgetincludes an HTML (Hypertext Markup Language) file, a CSS (CascadingStyle Sheets) file, and a JavaScript file. In some embodiments, a widgetincludes an XML (Extensible Markup Language) file and a JavaScript file(e.g., Yahoo! Widgets).

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, and browser module 247, the widget creator module 250are used by a user to create widgets (e.g., turning a user-specifiedportion of a web page into a widget).

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, search module 251 includes executable instructions to search fortext, music, sound, image, video, and/or other files in memory 202 thatmatch one or more search criteria (e.g., one or more user-specifiedsearch terms) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, audio circuitry 210,speaker 211, RF circuitry 208, and browser module 247, video and musicplayer module 252 includes executable instructions that allow the userto download and play back recorded music and other sound files stored inone or more file formats, such as MP3 or AAC files, and executableinstructions to display, present, or otherwise play back videos (e.g.,on touch screen 212 or on an external, connected display via externalport 224). In some embodiments, device 200 optionally includes thefunctionality of an MP3 player, such as an iPod (trademark of AppleInc.).

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, notes module 253 includes executable instructions to create andmanage notes, to-do lists, and the like in accordance with userinstructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, GPS module 235, and browser module 247, map module 254are used to receive, display, modify, and store maps and data associatedwith maps (e.g., driving directions, data on stores and other points ofinterest at or near a particular location, and other location-baseddata) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, audio circuitry 210,speaker 211, RF circuitry 208, text input module 234, e-mail clientmodule 240, and browser module 247, online video module 255 includesinstructions that allow the user to access, browse, receive (e.g., bystreaming and/or download), play back (e.g., on the touch screen or onan external, connected display via external port 224), send an e-mailwith a link to a particular online video, and otherwise manage onlinevideos in one or more file formats, such as H.264. In some embodiments,instant messaging module 241, rather than e-mail client module 240, isused to send a link to a particular online video. Additional descriptionof the online video application can be found in U.S. Provisional PatentApplication No. 60/936,562, “Portable Multifunction Device, Method, andGraphical User Interface for Playing Online Videos,” filed Jun. 20,2007, and U.S. patent application Ser. No. 11/968,067, “PortableMultifunction Device, Method, and Graphical User Interface for PlayingOnline Videos,” filed Dec. 31, 2007, the contents of which are herebyincorporated by reference in their entirety.

Each of the above-identified modules and applications corresponds to aset of executable instructions for performing one or more functionsdescribed above and the methods described in this application (e.g., thecomputer-implemented methods and other information processing methodsdescribed herein). These modules (e.g., sets of instructions) need notbe implemented as separate software programs, procedures, or modules,and thus various subsets of these modules can be combined or otherwiserearranged in various embodiments. For example, video player module canbe combined with music player module into a single module (e.g., videoand music player module 252, FIG. 2A). In some embodiments, memory 202stores a subset of the modules and data structures identified above.Furthermore, memory 202 stores additional modules and data structuresnot described above.

In some embodiments, device 200 is a device where operation of apredefined set of functions on the device is performed exclusivelythrough a touch screen and/or a touchpad. By using a touch screen and/ora touchpad as the primary input control device for operation of device200, the number of physical input control devices (such as push buttons,dials, and the like) on device 200 is reduced.

The predefined set of functions that are performed exclusively through atouch screen and/or a touchpad optionally include navigation betweenuser interfaces. In some embodiments, the touchpad, when touched by theuser, navigates device 200 to a main, home, or root menu from any userinterface that is displayed on device 200. In such embodiments, a “menubutton” is implemented using a touchpad. In some other embodiments, themenu button is a physical push button or other physical input controldevice instead of a touchpad.

FIG. 2B is a block diagram illustrating exemplary components for eventhandling in accordance with some embodiments. In some embodiments,memory 202 (FIG. 2A) or 470 (FIG. 4) includes event sorter 270 (e.g., inoperating system 226) and a respective application 236-1 (e.g., any ofthe aforementioned applications 237-251, 255, 480-490).

Event sorter 270 receives event information and determines theapplication 236-1 and application view 291 of application 236-1 to whichto deliver the event information. Event sorter 270 includes eventmonitor 271 and event dispatcher module 274. In some embodiments,application 236-1 includes application internal state 292, whichindicates the current application view(s) displayed on touch-sensitivedisplay 212 when the application is active or executing. In someembodiments, device/global internal state 257 is used by event sorter270 to determine which application(s) is (are) currently active, andapplication internal state 292 is used by event sorter 270 to determineapplication views 291 to which to deliver event information.

In some embodiments, application internal state 292 includes additionalinformation, such as one or more of: resume information to be used whenapplication 236-1 resumes execution, user interface state informationthat indicates information being displayed or that is ready for displayby application 236-1, a state queue for enabling the user to go back toa prior state or view of application 236-1, and a redo/undo queue ofprevious actions taken by the user.

Event monitor 271 receives event information from peripherals interface218. Event information includes information about a sub-event (e.g., auser touch on touch-sensitive display 212, as part of a multi-touchgesture). Peripherals interface 218 transmits information it receivesfrom I/O subsystem 206 or a sensor, such as proximity sensor 266,accelerometer(s) 268, and/or microphone 213 (through audio circuitry210). Information that peripherals interface 218 receives from I/Osubsystem 206 includes information from touch-sensitive display 212 or atouch-sensitive surface.

In some embodiments, event monitor 271 sends requests to the peripheralsinterface 218 at predetermined intervals. In response, peripheralsinterface 218 transmits event information. In other embodiments,peripherals interface 218 transmits event information only when there isa significant event (e.g., receiving an input above a predeterminednoise threshold and/or for more than a predetermined duration).

In some embodiments, event sorter 270 also includes a hit viewdetermination module 272 and/or an active event recognizer determinationmodule 273.

Hit view determination module 272 provides software procedures fordetermining where a sub-event has taken place within one or more viewswhen touch-sensitive display 212 displays more than one view. Views aremade up of controls and other elements that a user can see on thedisplay.

Another aspect of the user interface associated with an application is aset of views, sometimes herein called application views or userinterface windows, in which information is displayed and touch-basedgestures occur. The application views (of a respective application) inwhich a touch is detected correspond to programmatic levels within aprogrammatic or view hierarchy of the application. For example, thelowest level view in which a touch is detected is called the hit view,and the set of events that are recognized as proper inputs is determinedbased, at least in part, on the hit view of the initial touch thatbegins a touch-based gesture.

Hit view determination module 272 receives information related to subevents of a touch-based gesture. When an application has multiple viewsorganized in a hierarchy, hit view determination module 272 identifies ahit view as the lowest view in the hierarchy which should handle thesub-event. In most circumstances, the hit view is the lowest level viewin which an initiating sub-event occurs (e.g., the first sub-event inthe sequence of sub-events that form an event or potential event). Oncethe hit view is identified by the hit view determination module 272, thehit view typically receives all sub-events related to the same touch orinput source for which it was identified as the hit view.

Active event recognizer determination module 273 determines which viewor views within a view hierarchy should receive a particular sequence ofsub-events. In some embodiments, active event recognizer determinationmodule 273 determines that only the hit view should receive a particularsequence of sub-events. In other embodiments, active event recognizerdetermination module 273 determines that all views that include thephysical location of a sub-event are actively involved views, andtherefore determines that all actively involved views should receive aparticular sequence of sub-events. In other embodiments, even if touchsub-events were entirely confined to the area associated with oneparticular view, views higher in the hierarchy would still remain asactively involved views.

Event dispatcher module 274 dispatches the event information to an eventrecognizer (e.g., event recognizer 280). In embodiments including activeevent recognizer determination module 273, event dispatcher module 274delivers the event information to an event recognizer determined byactive event recognizer determination module 273. In some embodiments,event dispatcher module 274 stores in an event queue the eventinformation, which is retrieved by a respective event receiver 282.

In some embodiments, operating system 226 includes event sorter 270.Alternatively, application 236-1 includes event sorter 270. In yet otherembodiments, event sorter 270 is a stand-alone module, or a part ofanother module stored in memory 202, such as contact/motion module 230.

In some embodiments, application 236-1 includes a plurality of eventhandlers 290 and one or more application views 291, each of whichincludes instructions for handling touch events that occur within arespective view of the application's user interface. Each applicationview 291 of the application 236-1 includes one or more event recognizers280. Typically, a respective application view 291 includes a pluralityof event recognizers 280. In other embodiments, one or more of eventrecognizers 280 are part of a separate module, such as a user interfacekit (not shown) or a higher level object from which application 236-1inherits methods and other properties. In some embodiments, a respectiveevent handler 290 includes one or more of: data updater 276, objectupdater 277, GUI updater 278, and/or event data 279 received from eventsorter 270. Event handler 290 utilizes or calls data updater 276, objectupdater 277, or GUI updater 278 to update the application internal state292. Alternatively, one or more of the application views 291 include oneor more respective event handlers 290. Also, in some embodiments, one ormore of data updater 276, object updater 277, and GUI updater 278 areincluded in a respective application view 291.

A respective event recognizer 280 receives event information (e.g.,event data 279) from event sorter 270 and identifies an event from theevent information. Event recognizer 280 includes event receiver 282 andevent comparator 284. In some embodiments, event recognizer 280 alsoincludes at least a subset of: metadata 283, and event deliveryinstructions 288 (which include sub-event delivery instructions).

Event receiver 282 receives event information from event sorter 270. Theevent information includes information about a sub-event, for example, atouch or a touch movement. Depending on the sub-event, the eventinformation also includes additional information, such as location ofthe sub-event. When the sub-event concerns motion of a touch, the eventinformation also includes speed and direction of the sub-event. In someembodiments, events include rotation of the device from one orientationto another (e.g., from a portrait orientation to a landscapeorientation, or vice versa), and the event information includescorresponding information about the current orientation (also calleddevice attitude) of the device.

Event comparator 284 compares the event information to predefined eventor sub-event definitions and, based on the comparison, determines anevent or sub event, or determines or updates the state of an event orsub-event. In some embodiments, event comparator 284 includes eventdefinitions 286. Event definitions 286 contain definitions of events(e.g., predefined sequences of sub-events), for example, event 1(287-1), event 2 (287-2), and others. In some embodiments, sub-events inan event (287) include, for example, touch begin, touch end, touchmovement, touch cancellation, and multiple touching. In one example, thedefinition for event 1 (287-1) is a double tap on a displayed object.The double tap, for example, comprises a first touch (touch begin) onthe displayed object for a predetermined phase, a first liftoff (touchend) for a predetermined phase, a second touch (touch begin) on thedisplayed object for a predetermined phase, and a second liftoff (touchend) for a predetermined phase. In another example, the definition forevent 2 (287-2) is a dragging on a displayed object. The dragging, forexample, comprises a touch (or contact) on the displayed object for apredetermined phase, a movement of the touch across touch-sensitivedisplay 212, and liftoff of the touch (touch end). In some embodiments,the event also includes information for one or more associated eventhandlers 290.

In some embodiments, event definition 287 includes a definition of anevent for a respective user-interface object. In some embodiments, eventcomparator 284 performs a hit test to determine which user-interfaceobject is associated with a sub-event. For example, in an applicationview in which three user-interface objects are displayed ontouch-sensitive display 212, when a touch is detected on touch-sensitivedisplay 212, event comparator 284 performs a hit test to determine whichof the three user-interface objects is associated with the touch(sub-event). If each displayed object is associated with a respectiveevent handler 290, the event comparator uses the result of the hit testto determine which event handler 290 should be activated. For example,event comparator 284 selects an event handler associated with thesub-event and the object triggering the hit test.

In some embodiments, the definition for a respective event (287) alsoincludes delayed actions that delay delivery of the event informationuntil after it has been determined whether the sequence of sub-eventsdoes or does not correspond to the event recognizer's event type.

When a respective event recognizer 280 determines that the series ofsub-events do not match any of the events in event definitions 286, therespective event recognizer 280 enters an event impossible, eventfailed, or event ended state, after which it disregards subsequentsub-events of the touch-based gesture. In this situation, other eventrecognizers, if any, that remain active for the hit view continue totrack and process sub-events of an ongoing touch-based gesture.

In some embodiments, a respective event recognizer 280 includes metadata283 with configurable properties, flags, and/or lists that indicate howthe event delivery system should perform sub-event delivery to activelyinvolved event recognizers. In some embodiments, metadata 283 includesconfigurable properties, flags, and/or lists that indicate how eventrecognizers interact, or are enabled to interact, with one another. Insome embodiments, metadata 283 includes configurable properties, flags,and/or lists that indicate whether sub-events are delivered to varyinglevels in the view or programmatic hierarchy.

In some embodiments, a respective event recognizer 280 activates eventhandler 290 associated with an event when one or more particularsub-events of an event are recognized. In some embodiments, a respectiveevent recognizer 280 delivers event information associated with theevent to event handler 290. Activating an event handler 290 is distinctfrom sending (and deferred sending) sub-events to a respective hit view.In some embodiments, event recognizer 280 throws a flag associated withthe recognized event, and event handler 290 associated with the flagcatches the flag and performs a predefined process.

In some embodiments, event delivery instructions 288 include sub-eventdelivery instructions that deliver event information about a sub-eventwithout activating an event handler. Instead, the sub-event deliveryinstructions deliver event information to event handlers associated withthe series of sub-events or to actively involved views. Event handlersassociated with the series of sub-events or with actively involved viewsreceive the event information and perform a predetermined process.

In some embodiments, data updater 276 creates and updates data used inapplication 236-1. For example, data updater 276 updates the telephonenumber used in contacts module 237, or stores a video file used in videoplayer module. In some embodiments, object updater 277 creates andupdates objects used in application 236-1. For example, object updater277 creates a new user-interface object or updates the position of auser-interface object. GUI updater 278 updates the GUI. For example, GUIupdater 278 prepares display information and sends it to graphics module232 for display on a touch-sensitive display.

In some embodiments, event handler(s) 290 includes or has access to dataupdater 276, object updater 277, and GUI updater 278. In someembodiments, data updater 276, object updater 277, and GUI updater 278are included in a single module of a respective application 236-1 orapplication view 291. In other embodiments, they are included in two ormore software modules.

It shall be understood that the foregoing discussion regarding eventhandling of user touches on touch-sensitive displays also applies toother forms of user inputs to operate multifunction devices 200 withinput devices, not all of which are initiated on touch screens. Forexample, mouse movement and mouse button presses, optionally coordinatedwith single or multiple keyboard presses or holds; contact movementssuch as taps, drags, scrolls, etc. on touchpads; pen stylus inputs;movement of the device; oral instructions; detected eye movements;biometric inputs; and/or any combination thereof are optionally utilizedas inputs corresponding to sub-events which define an event to berecognized.

FIG. 3 illustrates a portable multifunction device 200 having a touchscreen 212 in accordance with some embodiments. The touch screenoptionally displays one or more graphics within user interface (UI) 300.In this embodiment, as well as others described below, a user is enabledto select one or more of the graphics by making a gesture on thegraphics, for example, with one or more fingers 302 (not drawn to scalein the figure) or one or more styluses 303 (not drawn to scale in thefigure). In some embodiments, selection of one or more graphics occurswhen the user breaks contact with the one or more graphics. In someembodiments, the gesture optionally includes one or more taps, one ormore swipes (from left to right, right to left, upward and/or downward),and/or a rolling of a finger (from right to left, left to right, upwardand/or downward) that has made contact with device 200. In someimplementations or circumstances, inadvertent contact with a graphicdoes not select the graphic. For example, a swipe gesture that sweepsover an application icon optionally does not select the correspondingapplication when the gesture corresponding to selection is a tap.

Device 200 also includes one or more physical buttons, such as “home” ormenu button 304. As described previously, menu button 304 is used tonavigate to any application 236 in a set of applications that isexecuted on device 200. Alternatively, in some embodiments, the menubutton is implemented as a soft key in a GUI displayed on touch screen212.

In one embodiment, device 200 includes touch screen 212, menu button304, push button 306 for powering the device on/off and locking thedevice, volume adjustment button(s) 308, subscriber identity module(SIM) card slot 310, headset jack 312, and docking/charging externalport 224. Push button 306 is, optionally, used to turn the power on/offon the device by depressing the button and holding the button in thedepressed state for a predefined time interval; to lock the device bydepressing the button and releasing the button before the predefinedtime interval has elapsed; and/or to unlock the device or initiate anunlock process. In an alternative embodiment, device 200 also acceptsverbal input for activation or deactivation of some functions throughmicrophone 213. Device 200 also, optionally, includes one or morecontact intensity sensors 265 for detecting intensity of contacts ontouch screen 212 and/or one or more tactile output generators 267 forgenerating tactile outputs for a user of device 200.

FIG. 4 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface in accordance with someembodiments. Device 400 need not be portable. In some embodiments,device 400 is a laptop computer, a desktop computer, a tablet computer,a multimedia player device, a navigation device, an educational device(such as a child's learning toy), a gaming system, or a control device(e.g., a home or industrial controller). Device 400 typically includesone or more processing units (CPUs) 410, one or more network or othercommunications interfaces 460, memory 470, and one or more communicationbuses 420 for interconnecting these components. Communication buses 420optionally include circuitry (sometimes called a chipset) thatinterconnects and controls communications between system components.Device 400 includes input/output (I/O) interface 430 comprising display440, which is typically a touch screen display. I/O interface 430 alsooptionally includes a keyboard and/or mouse (or other pointing device)450 and touchpad 455, tactile output generator 457 for generatingtactile outputs on device 400 (e.g., similar to tactile outputgenerator(s) 267 described above with reference to FIG. 2A), sensors 459(e.g., optical, acceleration, proximity, touch-sensitive, and/or contactintensity sensors similar to contact intensity sensor(s) 265 describedabove with reference to FIG. 2A). Memory 470 includes high-speed randomaccess memory, such as DRAM, SRAM, DDR RAM, or other random access solidstate memory devices; and optionally includes non-volatile memory, suchas one or more magnetic disk storage devices, optical disk storagedevices, flash memory devices, or other non-volatile solid state storagedevices. Memory 470 optionally includes one or more storage devicesremotely located from CPU(s) 410. In some embodiments, memory 470 storesprograms, modules, and data structures analogous to the programs,modules, and data structures stored in memory 202 of portablemultifunction device 200 (FIG. 2A), or a subset thereof. Furthermore,memory 470 optionally stores additional programs, modules, and datastructures not present in memory 202 of portable multifunction device200. For example, memory 470 of device 400 optionally stores drawingmodule 480, presentation module 482, word processing module 484, websitecreation module 486, disk authoring module 488, and/or spreadsheetmodule 490, while memory 202 of portable multifunction device 200 (FIG.2A) optionally does not store these modules.

Each of the above-identified elements in FIG. 4 is, in some examples,stored in one or more of the previously mentioned memory devices. Eachof the above-identified modules corresponds to a set of instructions forperforming a function described above. The above-identified modules orprograms (e.g., sets of instructions) need not be implemented asseparate software programs, procedures, or modules, and thus varioussubsets of these modules are combined or otherwise rearranged in variousembodiments. In some embodiments, memory 470 stores a subset of themodules and data structures identified above. Furthermore, memory 470stores additional modules and data structures not described above.

Attention is now directed towards embodiments of user interfaces thatcan be implemented on, for example, portable multifunction device 200.

FIG. 5A illustrates an exemplary user interface for a menu ofapplications on portable multifunction device 200 in accordance withsome embodiments. Similar user interfaces are implemented on device 400.In some embodiments, user interface 500 includes the following elements,or a subset or superset thereof:

Signal strength indicator(s) 502 for wireless communication(s), such ascellular and Wi-Fi signals;

Time 504;

Bluetooth indicator 505;

Battery status indicator 506;

Tray 508 with icons for frequently used applications, such as:

-   -   Icon 516 for telephone module 238, labeled “Phone,” which        optionally includes an indicator 514 of the number of missed        calls or voicemail messages;    -   Icon 518 for e-mail client module 240, labeled “Mail,” which        optionally includes an indicator 510 of the number of unread        e-mails;    -   Icon 520 for browser module 247, labeled “Browser;” and    -   Icon 522 for video and music player module 252, also referred to        as iPod (trademark of Apple Inc.) module 252, labeled “iPod;”        and

Icons for other applications, such as:

-   -   Icon 524 for IM module 241, labeled “Messages;”    -   Icon 526 for calendar module 248, labeled “Calendar;”    -   Icon 528 for image management module 244, labeled “Photos;”    -   Icon 530 for camera module 243, labeled “Camera;”    -   Icon 532 for online video module 255, labeled “Online Video;”    -   Icon 534 for stocks widget 249-2, labeled “Stocks;”    -   Icon 536 for map module 254, labeled “Maps;”    -   Icon 538 for weather widget 249-1, labeled “Weather;”    -   Icon 540 for alarm clock widget 249-4, labeled “Clock;”    -   Icon 542 for workout support module 242, labeled “Workout        Support;”    -   Icon 544 for notes module 253, labeled “Notes;” and    -   Icon 546 for a settings application or module, labeled        “Settings,” which provides access to settings for device 200 and        its various applications 236.

It should be noted that the icon labels illustrated in FIG. 5A aremerely exemplary. For example, icon 522 for video and music playermodule 252 is optionally labeled “Music” or “Music Player.” Other labelsare, optionally, used for various application icons. In someembodiments, a label for a respective application icon includes a nameof an application corresponding to the respective application icon. Insome embodiments, a label for a particular application icon is distinctfrom a name of an application corresponding to the particularapplication icon.

FIG. 5B illustrates an exemplary user interface on a device (e.g.,device 400, FIG. 4) with a touch-sensitive surface 551 (e.g., a tabletor touchpad 455, FIG. 4) that is separate from the display 550 (e.g.,touch screen display 212). Device 400 also, optionally, includes one ormore contact intensity sensors (e.g., one or more of sensors 457) fordetecting intensity of contacts on touch-sensitive surface 551 and/orone or more tactile output generators 459 for generating tactile outputsfor a user of device 400.

Although some of the examples which follow will be given with referenceto inputs on touch screen display 212 (where the touch-sensitive surfaceand the display are combined), in some embodiments, the device detectsinputs on a touch-sensitive surface that is separate from the display,as shown in FIG. 5B. In some embodiments, the touch-sensitive surface(e.g., 551 in FIG. 5B) has a primary axis (e.g., 552 in FIG. 5B) thatcorresponds to a primary axis (e.g., 553 in FIG. 5B) on the display(e.g., 550). In accordance with these embodiments, the device detectscontacts (e.g., 560 and 562 in FIG. 5B) with the touch-sensitive surface551 at locations that correspond to respective locations on the display(e.g., in FIG. 5B, 560 corresponds to 568 and 562 corresponds to 570).In this way, user inputs (e.g., contacts 560 and 562, and movementsthereof) detected by the device on the touch-sensitive surface (e.g.,551 in FIG. 5B) are used by the device to manipulate the user interfaceon the display (e.g., 550 in FIG. 5B) of the multifunction device whenthe touch-sensitive surface is separate from the display. It should beunderstood that similar methods are, optionally, used for other userinterfaces described herein.

Additionally, while the following examples are given primarily withreference to finger inputs (e.g., finger contacts, finger tap gestures,finger swipe gestures), it should be understood that, in someembodiments, one or more of the finger inputs are replaced with inputfrom another input device (e.g., a mouse-based input or stylus input).For example, a swipe gesture is, optionally, replaced with a mouse click(e.g., instead of a contact) followed by movement of the cursor alongthe path of the swipe (e.g., instead of movement of the contact). Asanother example, a tap gesture is, optionally, replaced with a mouseclick while the cursor is located over the location of the tap gesture(e.g., instead of detection of the contact followed by ceasing to detectthe contact). Similarly, when multiple user inputs are simultaneouslydetected, it should be understood that multiple computer mice are,optionally, used simultaneously, or a mouse and finger contacts are,optionally, used simultaneously.

FIG. 6A illustrates exemplary personal electronic device 600. Device 600includes body 602. In some embodiments, device 600 includes some or allof the features described with respect to devices 200 and 400 (e.g.,FIGS. 2A-4). In some embodiments, device 600 has touch-sensitive displayscreen 604, hereafter touch screen 604. Alternatively, or in addition totouch screen 604, device 600 has a display and a touch-sensitivesurface. As with devices 200 and 400, in some embodiments, touch screen604 (or the touch-sensitive surface) has one or more intensity sensorsfor detecting intensity of contacts (e.g., touches) being applied. Theone or more intensity sensors of touch screen 604 (or thetouch-sensitive surface) provide output data that represents theintensity of touches. The user interface of device 600 responds totouches based on their intensity, meaning that touches of differentintensities can invoke different user interface operations on device600.

Techniques for detecting and processing touch intensity are found, forexample, in related applications: International Patent ApplicationSerial No. PCT/US2013/040061, titled “Device, Method, and Graphical UserInterface for Displaying User Interface Objects Corresponding to anApplication,” filed May 8, 2013, and International Patent ApplicationSerial No. PCT/US2013/069483, titled “Device, Method, and Graphical UserInterface for Transitioning Between Touch Input to Display OutputRelationships,” filed Nov. 11, 2013, each of which is herebyincorporated by reference in their entirety.

In some embodiments, device 600 has one or more input mechanisms 606 and608. Input mechanisms 606 and 608, if included, are physical. Examplesof physical input mechanisms include push buttons and rotatablemechanisms. In some embodiments, device 600 has one or more attachmentmechanisms. Such attachment mechanisms, if included, can permitattachment of device 600 with, for example, hats, eyewear, earrings,necklaces, shirts, jackets, bracelets, watch straps, chains, trousers,belts, shoes, purses, backpacks, and so forth. These attachmentmechanisms permit device 600 to be worn by a user.

FIG. 6B depicts exemplary personal electronic device 600. In someembodiments, device 600 includes some or all of the components describedwith respect to FIGS. 2A, 2B, and 4. Device 600 has bus 612 thatoperatively couples I/O section 614 with one or more computer processors616 and memory 618. I/O section 614 is connected to display 604, whichcan have touch-sensitive component 622 and, optionally, touch-intensitysensitive component 624. In addition, I/O section 614 is connected withcommunication unit 630 for receiving application and operating systemdata, using Wi-Fi, Bluetooth, near field communication (NFC), cellular,and/or other wireless communication techniques. Device 600 includesinput mechanisms 606 and/or 608. Input mechanism 606 is a rotatableinput device or a depressible and rotatable input device, for example.Input mechanism 608 is a button, in some examples.

Input mechanism 608 is a microphone, in some examples. Personalelectronic device 600 includes, for example, various sensors, such asGPS sensor 632, accelerometer 634, directional sensor 640 (e.g.,compass), gyroscope 636, motion sensor 638, and/or a combinationthereof, all of which are operatively connected to I/O section 614.

Memory 618 of personal electronic device 600 is a non-transitorycomputer-readable storage medium, for storing computer-executableinstructions, which, when executed by one or more computer processors616, for example, cause the computer processors to perform thetechniques and processes described below. The computer-executableinstructions, for example, are also stored and/or transported within anynon-transitory computer-readable storage medium for use by or inconnection with an instruction execution system, apparatus, or device,such as a computer-based system, processor-containing system, or othersystem that can fetch the instructions from the instruction executionsystem, apparatus, or device and execute the instructions. Personalelectronic device 600 is not limited to the components and configurationof FIG. 6B, but can include other or additional components in multipleconfigurations.

As used here, the term “affordance” refers to a user-interactivegraphical user interface object that is, for example, displayed on thedisplay screen of devices 200, 400, and/or 600 (FIGS. 2A, 4, and 6A-6B).For example, an image (e.g., icon), a button, and text (e.g., hyperlink)each constitutes an affordance.

As used herein, the term “focus selector” refers to an input elementthat indicates a current part of a user interface with which a user isinteracting. In some implementations that include a cursor or otherlocation marker, the cursor acts as a “focus selector” so that when aninput (e.g., a press input) is detected on a touch-sensitive surface(e.g., touchpad 455 in FIG. 4 or touch-sensitive surface 551 in FIG. 5B)while the cursor is over a particular user interface element (e.g., abutton, window, slider or other user interface element), the particularuser interface element is adjusted in accordance with the detectedinput. In some implementations that include a touch screen display(e.g., touch-sensitive display system 212 in FIG. 2A or touch screen 212in FIG. 5A) that enables direct interaction with user interface elementson the touch screen display, a detected contact on the touch screen actsas a “focus selector” so that when an input (e.g., a press input by thecontact) is detected on the touch screen display at a location of aparticular user interface element (e.g., a button, window, slider, orother user interface element), the particular user interface element isadjusted in accordance with the detected input. In some implementations,focus is moved from one region of a user interface to another region ofthe user interface without corresponding movement of a cursor ormovement of a contact on a touch screen display (e.g., by using a tabkey or arrow keys to move focus from one button to another button); inthese implementations, the focus selector moves in accordance withmovement of focus between different regions of the user interface.Without regard to the specific form taken by the focus selector, thefocus selector is generally the user interface element (or contact on atouch screen display) that is controlled by the user so as tocommunicate the user's intended interaction with the user interface(e.g., by indicating, to the device, the element of the user interfacewith which the user is intending to interact). For example, the locationof a focus selector (e.g., a cursor, a contact, or a selection box) overa respective button while a press input is detected on thetouch-sensitive surface (e.g., a touchpad or touch screen) will indicatethat the user is intending to activate the respective button (as opposedto other user interface elements shown on a display of the device).

As used in the specification and claims, the term “characteristicintensity” of a contact refers to a characteristic of the contact basedon one or more intensities of the contact. In some embodiments, thecharacteristic intensity is based on multiple intensity samples. Thecharacteristic intensity is, optionally, based on a predefined number ofintensity samples, or a set of intensity samples collected during apredetermined time period (e.g., 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10seconds) relative to a predefined event (e.g., after detecting thecontact, prior to detecting liftoff of the contact, before or afterdetecting a start of movement of the contact, prior to detecting an endof the contact, before or after detecting an increase in intensity ofthe contact, and/or before or after detecting a decrease in intensity ofthe contact). A characteristic intensity of a contact is, optionallybased on one or more of: a maximum value of the intensities of thecontact, a mean value of the intensities of the contact, an averagevalue of the intensities of the contact, a top 10 percentile value ofthe intensities of the contact, a value at the half maximum of theintensities of the contact, a value at the 90 percent maximum of theintensities of the contact, or the like. In some embodiments, theduration of the contact is used in determining the characteristicintensity (e.g., when the characteristic intensity is an average of theintensity of the contact over time). In some embodiments, thecharacteristic intensity is compared to a set of one or more intensitythresholds to determine whether an operation has been performed by auser. For example, the set of one or more intensity thresholds includesa first intensity threshold and a second intensity threshold. In thisexample, a contact with a characteristic intensity that does not exceedthe first threshold results in a first operation, a contact with acharacteristic intensity that exceeds the first intensity threshold anddoes not exceed the second intensity threshold results in a secondoperation, and a contact with a characteristic intensity that exceedsthe second threshold results in a third operation. In some embodiments,a comparison between the characteristic intensity and one or morethresholds is used to determine whether or not to perform one or moreoperations (e.g., whether to perform a respective operation or forgoperforming the respective operation) rather than being used to determinewhether to perform a first operation or a second operation.

In some embodiments, a portion of a gesture is identified for purposesof determining a characteristic intensity. For example, atouch-sensitive surface receives a continuous swipe contacttransitioning from a start location and reaching an end location, atwhich point the intensity of the contact increases. In this example, thecharacteristic intensity of the contact at the end location is based ononly a portion of the continuous swipe contact, and not the entire swipecontact (e.g., only the portion of the swipe contact at the endlocation). In some embodiments, a smoothing algorithm is applied to theintensities of the swipe contact prior to determining the characteristicintensity of the contact. For example, the smoothing algorithmoptionally includes one or more of: an unweighted sliding-averagesmoothing algorithm, a triangular smoothing algorithm, a median filtersmoothing algorithm, and/or an exponential smoothing algorithm. In somecircumstances, these smoothing algorithms eliminate narrow spikes ordips in the intensities of the swipe contact for purposes of determininga characteristic intensity.

The intensity of a contact on the touch-sensitive surface ischaracterized relative to one or more intensity thresholds, such as acontact-detection intensity threshold, a light press intensitythreshold, a deep press intensity threshold, and/or one or more otherintensity thresholds. In some embodiments, the light press intensitythreshold corresponds to an intensity at which the device will performoperations typically associated with clicking a button of a physicalmouse or a trackpad. In some embodiments, the deep press intensitythreshold corresponds to an intensity at which the device will performoperations that are different from operations typically associated withclicking a button of a physical mouse or a trackpad. In someembodiments, when a contact is detected with a characteristic intensitybelow the light press intensity threshold (e.g., and above a nominalcontact-detection intensity threshold below which the contact is nolonger detected), the device will move a focus selector in accordancewith movement of the contact on the touch-sensitive surface withoutperforming an operation associated with the light press intensitythreshold or the deep press intensity threshold. Generally, unlessotherwise stated, these intensity thresholds are consistent betweendifferent sets of user interface figures.

An increase of characteristic intensity of the contact from an intensitybelow the light press intensity threshold to an intensity between thelight press intensity threshold and the deep press intensity thresholdis sometimes referred to as a “light press” input. An increase ofcharacteristic intensity of the contact from an intensity below the deeppress intensity threshold to an intensity above the deep press intensitythreshold is sometimes referred to as a “deep press” input. An increaseof characteristic intensity of the contact from an intensity below thecontact-detection intensity threshold to an intensity between thecontact-detection intensity threshold and the light press intensitythreshold is sometimes referred to as detecting the contact on thetouch-surface. A decrease of characteristic intensity of the contactfrom an intensity above the contact-detection intensity threshold to anintensity below the contact-detection intensity threshold is sometimesreferred to as detecting liftoff of the contact from the touch-surface.In some embodiments, the contact-detection intensity threshold is zero.In some embodiments, the contact-detection intensity threshold isgreater than zero.

In some embodiments described herein, one or more operations areperformed in response to detecting a gesture that includes a respectivepress input or in response to detecting the respective press inputperformed with a respective contact (or a plurality of contacts), wherethe respective press input is detected based at least in part ondetecting an increase in intensity of the contact (or plurality ofcontacts) above a press-input intensity threshold. In some embodiments,the respective operation is performed in response to detecting theincrease in intensity of the respective contact above the press-inputintensity threshold (e.g., a “down stroke” of the respective pressinput). In some embodiments, the press input includes an increase inintensity of the respective contact above the press-input intensitythreshold and a subsequent decrease in intensity of the contact belowthe press-input intensity threshold, and the respective operation isperformed in response to detecting the subsequent decrease in intensityof the respective contact below the press-input threshold (e.g., an “upstroke” of the respective press input).

In some embodiments, the device employs intensity hysteresis to avoidaccidental inputs sometimes termed “jitter,” where the device defines orselects a hysteresis intensity threshold with a predefined relationshipto the press-input intensity threshold (e.g., the hysteresis intensitythreshold is X intensity units lower than the press-input intensitythreshold or the hysteresis intensity threshold is 75%, 90%, or somereasonable proportion of the press-input intensity threshold). Thus, insome embodiments, the press input includes an increase in intensity ofthe respective contact above the press-input intensity threshold and asubsequent decrease in intensity of the contact below the hysteresisintensity threshold that corresponds to the press-input intensitythreshold, and the respective operation is performed in response todetecting the subsequent decrease in intensity of the respective contactbelow the hysteresis intensity threshold (e.g., an “up stroke” of therespective press input). Similarly, in some embodiments, the press inputis detected only when the device detects an increase in intensity of thecontact from an intensity at or below the hysteresis intensity thresholdto an intensity at or above the press-input intensity threshold and,optionally, a subsequent decrease in intensity of the contact to anintensity at or below the hysteresis intensity, and the respectiveoperation is performed in response to detecting the press input (e.g.,the increase in intensity of the contact or the decrease in intensity ofthe contact, depending on the circumstances).

For ease of explanation, the descriptions of operations performed inresponse to a press input associated with a press-input intensitythreshold or in response to a gesture including the press input are,optionally, triggered in response to detecting either: an increase inintensity of a contact above the press-input intensity threshold, anincrease in intensity of a contact from an intensity below thehysteresis intensity threshold to an intensity above the press-inputintensity threshold, a decrease in intensity of the contact below thepress-input intensity threshold, and/or a decrease in intensity of thecontact below the hysteresis intensity threshold corresponding to thepress-input intensity threshold. Additionally, in examples where anoperation is described as being performed in response to detecting adecrease in intensity of a contact below the press-input intensitythreshold, the operation is, optionally, performed in response todetecting a decrease in intensity of the contact below a hysteresisintensity threshold corresponding to, and lower than, the press-inputintensity threshold.

3. Digital Assistant System

FIG. 7A illustrates a block diagram of digital assistant system 700 inaccordance with various examples. In some examples, digital assistantsystem 700 is implemented on a standalone computer system. In someexamples, digital assistant system 700 is distributed across multiplecomputers. In some examples, some of the modules and functions of thedigital assistant are divided into a server portion and a clientportion, where the client portion resides on one or more user devices(e.g., devices 104, 122, 200, 400, or 600) and communicates with theserver portion (e.g., server system 108) through one or more networks,e.g., as shown in FIG. 1. In some examples, digital assistant system 700is an implementation of server system 108 (and/or DA server 106) shownin FIG. 1. It should be noted that digital assistant system 700 is onlyone example of a digital assistant system, and that digital assistantsystem 700 can have more or fewer components than shown, can combine twoor more components, or can have a different configuration or arrangementof the components. The various components shown in FIG. 7A areimplemented in hardware, software instructions for execution by one ormore processors, firmware, including one or more signal processingand/or application specific integrated circuits, or a combinationthereof.

Digital assistant system 700 includes memory 702, one or more processors704, input/output (1/O) interface 706, and network communicationsinterface 708. These components can communicate with one another overone or more communication buses or signal lines 710.

In some examples, memory 702 includes a non-transitory computer-readablemedium, such as high-speed random access memory and/or a non-volatilecomputer-readable storage medium (e.g., one or more magnetic diskstorage devices, flash memory devices, or other non-volatile solid-statememory devices).

In some examples, I/O interface 706 couples input/output devices 716 ofdigital assistant system 700, such as displays, keyboards, touchscreens, and microphones, to user interface module 722. I/O interface706, in conjunction with user interface module 722, receives user inputs(e.g., voice input, keyboard inputs, touch inputs, etc.) and processesthem accordingly. In some examples, e.g., when the digital assistant isimplemented on a standalone user device, digital assistant system 700includes any of the components and/O communication interfaces describedwith respect to devices 200, 400, or 600 in FIGS. 2A, 4, 6A-6B,respectively. In some examples, digital assistant system 700 representsthe server portion of a digital assistant implementation, and caninteract with the user through a client-side portion residing on a userdevice (e.g., devices 104, 200, 400, or 600).

In some examples, the network communications interface 708 includeswired communication port(s) 712 and/or wireless transmission andreception circuitry 714. The wired communication port(s) receives andsend communication signals via one or more wired interfaces, e.g.,Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wirelesscircuitry 714 receives and sends RF signals and/or optical signalsfrom/to communications networks and other communications devices. Thewireless communications use any of a plurality of communicationsstandards, protocols, and technologies, such as GSM, EDGE, CDMA, TDMA,Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communicationprotocol. Network communications interface 708 enables communicationbetween digital assistant system 700 with networks, such as theInternet, an intranet, and/or a wireless network, such as a cellulartelephone network, a wireless local area network (LAN), and/or ametropolitan area network (MAN), and other devices.

In some examples, memory 702, or the computer-readable storage media ofmemory 702, stores programs, modules, instructions, and data structuresincluding all or a subset of: operating system 718, communicationsmodule 720, user interface module 722, one or more applications 724, anddigital assistant module 726. In particular, memory 702, or thecomputer-readable storage media of memory 702, stores instructions forperforming the processes described below. One or more processors 704execute these programs, modules, and instructions, and reads/writesfrom/to the data structures.

Operating system 718 (e.g., Darwin, RTXC, LINUX, UNIX, iOS, OS X,WINDOWS, or an embedded operating system such as VxWorks) includesvarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communications between varioushardware, firmware, and software components.

Communications module 720 facilitates communications between digitalassistant system 700 with other devices over network communicationsinterface 708. For example, communications module 720 communicates withRF circuitry 208 of electronic devices such as devices 200, 400, and 600shown in FIGS. 2A, 4, 6A-6B, respectively. Communications module 720also includes various components for handling data received by wirelesscircuitry 714 and/or wired communications port 712.

User interface module 722 receives commands and/or inputs from a uservia I/O interface 706 (e.g., from a keyboard, touch screen, pointingdevice, controller, and/or microphone), and generate user interfaceobjects on a display. User interface module 722 also prepares anddelivers outputs (e.g., speech, sound, animation, text, icons,vibrations, haptic feedback, light, etc.) to the user via the I/Ointerface 706 (e.g., through displays, audio channels, speakers,touch-pads, etc.).

Applications 724 include programs and/or modules that are configured tobe executed by one or more processors 704. For example, if the digitalassistant system is implemented on a standalone user device,applications 724 include user applications, such as games, a calendarapplication, a navigation application, or an email application. Ifdigital assistant system 700 is implemented on a server, applications724 include resource management applications, diagnostic applications,or scheduling applications, for example.

Memory 702 also stores digital assistant module 726 (or the serverportion of a digital assistant). In some examples, digital assistantmodule 726 includes the following sub-modules, or a subset or supersetthereof: input/output processing module 728, speech-to-text (STT)processing module 730, natural language processing module 732, dialogueflow processing module 734, task flow processing module 736, serviceprocessing module 738, and speech synthesis processing module 740. Eachof these modules has access to one or more of the following systems ordata and models of the digital assistant module 726, or a subset orsuperset thereof: ontology 760, vocabulary index 744, user data 748,task flow models 754, service models 756, and ASR systems 758.

In some examples, using the processing modules, data, and modelsimplemented in digital assistant module 726, the digital assistant canperform at least some of the following: converting speech input intotext; identifying a user's intent expressed in a natural language inputreceived from the user; actively eliciting and obtaining informationneeded to fully infer the user's intent (e.g., by disambiguating words,games, intentions, etc.); determining the task flow for fulfilling theinferred intent; and executing the task flow to fulfill the inferredintent.

In some examples, as shown in FIG. 7B, I/O processing module 728interacts with the user through I/O devices 716 in FIG. 7A or with auser device (e.g., devices 104, 200, 400, or 600) through networkcommunications interface 708 in FIG. 7A to obtain user input (e.g., aspeech input) and to provide responses (e.g., as speech outputs) to theuser input. I/O processing module 728 optionally obtains contextualinformation associated with the user input from the user device, alongwith or shortly after the receipt of the user input. The contextualinformation includes user-specific data, vocabulary, and/or preferencesrelevant to the user input. In some examples, the contextual informationalso includes software and hardware states of the user device at thetime the user request is received, and/or information related to thesurrounding environment of the user at the time that the user requestwas received. In some examples, I/O processing module 728 also sendsfollow-up questions to, and receive answers from, the user regarding theuser request. When a user request is received by I/O processing module728 and the user request includes speech input, I/O processing module728 forwards the speech input to STT processing module 730 (or speechrecognizer) for speech-to-text conversions.

STT processing module 730 includes one or more ASR systems 758. The oneor more ASR systems 758 can process the speech input that is receivedthrough I/O processing module 728 to produce a recognition result. EachASR system 758 includes a front-end speech pre-processor. The front-endspeech pre-processor extracts representative features from the speechinput. For example, the front-end speech pre-processor performs aFourier transform on the speech input to extract spectral features thatcharacterize the speech input as a sequence of representativemulti-dimensional vectors. Further, each ASR system 758 includes one ormore speech recognition models (e.g., acoustic models and/or languagemodels) and implements one or more speech recognition engines. Examplesof speech recognition models include Hidden Markov Models,Gaussian-Mixture Models, Deep Neural Network Models, n-gram languagemodels, and other statistical models. Examples of speech recognitionengines include the dynamic time warping based engines and weightedfinite-state transducers (WFST) based engines. The one or more speechrecognition models and the one or more speech recognition engines areused to process the extracted representative features of the front-endspeech pre-processor to produce intermediate recognitions results (e.g.,phonemes, phonemic strings, and sub-words), and ultimately, textrecognition results (e.g., words, word strings, or sequence of tokens).In some examples, the speech input is processed at least partially by athird-party service or on the user's device (e.g., device 104, 200, 400,or 600) to produce the recognition result. Once STT processing module730 produces recognition results containing a text string (e.g., words,or sequence of words, or sequence of tokens), the recognition result ispassed to natural language processing module 732 for intent deduction.In some examples, STT processing module 730 produces multiple candidatetext representations of the speech input. Each candidate textrepresentation is a sequence of words or tokens corresponding to thespeech input. In some examples, each candidate text representation isassociated with a speech recognition confidence score. Based on thespeech recognition confidence scores, STT processing module 730 ranksthe candidate text representations and provides the n-best (e.g., nhighest ranked) candidate text representation(s) to natural languageprocessing module 732 for intent deduction, where n is a predeterminedinteger greater than zero. For example, in one example, only the highestranked (n=1) candidate text representation is passed to natural languageprocessing module 732 for intent deduction. In another example, the fivehighest ranked (n=5) candidate text representations are passed tonatural language processing module 732 for intent deduction.

More details on the speech-to-text processing are described in U.S.Utility application Ser. No. 13/236,942 for “Consolidating SpeechRecognition Results,” filed on Sep. 20, 2011, the entire disclosure ofwhich is incorporated herein by reference.

In some examples, STT processing module 730 includes and/or accesses avocabulary of recognizable words via phonetic alphabet conversion module731. Each vocabulary word is associated with one or more candidatepronunciations of the word represented in a speech recognition phoneticalphabet. In particular, the vocabulary of recognizable words includes aword that is associated with a plurality of candidate pronunciations.For example, the vocabulary includes the word “tomato” that isassociated with the candidate pronunciations of /

/ and /

/. Further, vocabulary words are associated with custom candidatepronunciations that are based on previous speech inputs from the user.Such custom candidate pronunciations are stored in STT processing module730 and are associated with a particular user via the user's profile onthe device. In some examples, the candidate pronunciations for words aredetermined based on the spelling of the word and one or more linguisticand/or phonetic rules. In some examples, the candidate pronunciationsare manually generated, e.g., based on known canonical pronunciations.

In some examples, the candidate pronunciations are ranked based on thecommonness of the candidate pronunciation. For example, the candidatepronunciation /

/ is ranked higher than /

/, because the former is a more commonly used pronunciation (e.g., amongall users, for users in a particular geographical region, or for anyother appropriate subset of users). In some examples, candidatepronunciations are ranked based on whether the candidate pronunciationis a custom candidate pronunciation associated with the user. Forexample, custom candidate pronunciations are ranked higher thancanonical candidate pronunciations. This can be useful for recognizingproper nouns having a unique pronunciation that deviates from canonicalpronunciation. In some examples, candidate pronunciations are associatedwith one or more speech characteristics, such as geographic origin,nationality, or ethnicity. For example, the candidate pronunciation /

/ is associated with the United States, whereas the candidatepronunciation /

/ is associated with Great Britain. Further, the rank of the candidatepronunciation is based on one or more characteristics (e.g., geographicorigin, nationality, ethnicity, etc.) of the user stored in the user'sprofile on the device. For example, it can be determined from the user'sprofile that the user is associated with the United States. Based on theuser being associated with the United States, the candidatepronunciation /

/ (associated with the United States) is ranked higher than thecandidate pronunciation /

/ (associated with Great Britain). In some examples, one of the rankedcandidate pronunciations is selected as a predicted pronunciation (e.g.,the most likely pronunciation).

When a speech input is received, STT processing module 730 is used todetermine the phonemes corresponding to the speech input (e.g., using anacoustic model), and then attempt to determine words that match thephonemes (e.g., using a language model). For example, if STT processingmodule 730 first identifies the sequence of phonemes /

/ corresponding to a portion of the speech input, it can then determine,based on vocabulary index 744, that this sequence corresponds to theword “tomato.”

In some examples, STT processing module 730 uses approximate matchingtechniques to determine words in an utterance. Thus, for example, theSTT processing module 730 determines that the sequence of phonemes /

/ corresponds to the word “tomato,” even if that particular sequence ofphonemes is not one of the candidate sequence of phonemes for that word.

Natural language processing module 732 (“natural language processor”) ofthe digital assistant takes the n-best candidate text representation(s)(“word sequence(s)” or “token sequence(s)”) generated by STT processingmodule 730, and attempts to associate each of the candidate textrepresentations with one or more “actionable intents” recognized by thedigital assistant. An “actionable intent” (or “user intent”) representsa task that can be performed by the digital assistant, and can have anassociated task flow implemented in task flow models 754. The associatedtask flow is a series of programmed actions and steps that the digitalassistant takes in order to perform the task. The scope of a digitalassistant's capabilities is dependent on the number and variety of taskflows that have been implemented and stored in task flow models 754, orin other words, on the number and variety of “actionable intents” thatthe digital assistant recognizes. The effectiveness of the digitalassistant, however, also dependents on the assistant's ability to inferthe correct “actionable intent(s)” from the user request expressed innatural language.

In some examples, in addition to the sequence of words or tokensobtained from STT processing module 730, natural language processingmodule 732 also receives contextual information associated with the userrequest, e.g., from I/O processing module 728. The natural languageprocessing module 732 optionally uses the contextual information toclarify, supplement, and/or further define the information contained inthe candidate text representations received from STT processing module730. The contextual information includes, for example, user preferences,hardware, and/or software states of the user device, sensor informationcollected before, during, or shortly after the user request, priorinteractions (e.g., dialogue) between the digital assistant and theuser, and the like. As described herein, contextual information is, insome examples, dynamic, and changes with time, location, content of thedialogue, and other factors.

In some examples, the natural language processing is based on, e.g.,ontology 760. Ontology 760 is a hierarchical structure containing manynodes, each node representing either an “actionable intent” or a“property” relevant to one or more of the “actionable intents” or other“properties.” As noted above, an “actionable intent” represents a taskthat the digital assistant is capable of performing, i.e., it is“actionable” or can be acted on. A “property” represents a parameterassociated with an actionable intent or a sub-aspect of anotherproperty. A linkage between an actionable intent node and a propertynode in ontology 760 defines how a parameter represented by the propertynode pertains to the task represented by the actionable intent node.

In some examples, ontology 760 is made up of actionable intent nodes andproperty nodes. Within ontology 760, each actionable intent node islinked to one or more property nodes either directly or through one ormore intermediate property nodes. Similarly, each property node islinked to one or more actionable intent nodes either directly or throughone or more intermediate property nodes. For example, as shown in FIG.7C, ontology 760 includes a “restaurant reservation” node (i.e., anactionable intent node). Property nodes “restaurant,” “date/time” (forthe reservation), and “party size” are each directly linked to theactionable intent node (i.e., the “restaurant reservation” node).

In addition, property nodes “cuisine,” “price range,” “phone number,”and “location” are sub-nodes of the property node “restaurant,” and areeach linked to the “restaurant reservation” node (i.e., the actionableintent node) through the intermediate property node “restaurant.” Foranother example, as shown in FIG. 7C, ontology 760 also includes a “setreminder” node (i.e., another actionable intent node). Property nodes“date/time” (for setting the reminder) and “subject” (for the reminder)are each linked to the “set reminder” node. Since the property“date/time” is relevant to both the task of making a restaurantreservation and the task of setting a reminder, the property node“date/time” is linked to both the “restaurant reservation” node and the“set reminder” node in ontology 760.

An actionable intent node, along with its linked property nodes, isdescribed as a “domain.” In the present discussion, each domain isassociated with a respective actionable intent, and refers to the groupof nodes (and the relationships there between) associated with theparticular actionable intent. For example, ontology 760 shown in FIG. 7Cincludes an example of restaurant reservation domain 762 and an exampleof reminder domain 764 within ontology 760. The restaurant reservationdomain includes the actionable intent node “restaurant reservation,”property nodes “restaurant,” “date/time,” and “party size,” andsub-property nodes “cuisine,” “price range,” “phone number,” and“location.” Reminder domain 764 includes the actionable intent node “setreminder,” and property nodes “subject” and “date/time.” In someexamples, ontology 760 is made up of many domains. Each domain sharesone or more property nodes with one or more other domains. For example,the “date/time” property node is associated with many different domains(e.g., a scheduling domain, a travel reservation domain, a movie ticketdomain, etc.), in addition to restaurant reservation domain 762 andreminder domain 764.

While FIG. 7C illustrates two example domains within ontology 760, otherdomains include, for example, “find a movie,” “initiate a phone call,”“find directions,” “schedule a meeting,” “send a message,” and “providean answer to a question,” “read a list,” “providing navigationinstructions,” “provide instructions for a task” and so on. A “send amessage” domain is associated with a “send a message” actionable intentnode, and further includes property nodes such as “recipient(s),”“message type,” and “message body.” The property node “recipient” isfurther defined, for example, by the sub-property nodes such as“recipient name” and “message address.”

In some examples, ontology 760 includes all the domains (and henceactionable intents) that the digital assistant is capable ofunderstanding and acting upon. In some examples, ontology 760 ismodified, such as by adding or removing entire domains or nodes, or bymodifying relationships between the nodes within the ontology 760.

In some examples, nodes associated with multiple related actionableintents are clustered under a “super domain” in ontology 760. Forexample, a “travel” super-domain includes a cluster of property nodesand actionable intent nodes related to travel. The actionable intentnodes related to travel includes “airline reservation,” “hotelreservation,” “car rental,” “get directions,” “find points of interest,”and so on. The actionable intent nodes under the same super domain(e.g., the “travel” super domain) have many property nodes in common.For example, the actionable intent nodes for “airline reservation,”“hotel reservation,” “car rental,” “get directions,” and “find points ofinterest” share one or more of the property nodes “start location,”“destination,” “departure date/time,” “arrival date/time,” and “partysize.”

In some examples, each node in ontology 760 is associated with a set ofwords and/or phrases that are relevant to the property or actionableintent represented by the node. The respective set of words and/orphrases associated with each node are the so-called “vocabulary”associated with the node. The respective set of words and/or phrasesassociated with each node are stored in vocabulary index 744 inassociation with the property or actionable intent represented by thenode. For example, returning to FIG. 7B, the vocabulary associated withthe node for the property of “restaurant” includes words such as “food,”“drinks,” “cuisine,” “hungry,” “eat,” “pizza,” “fast food,” “meal,” andso on. For another example, the vocabulary associated with the node forthe actionable intent of “initiate a phone call” includes words andphrases such as “call,” “phone,” “dial,” “ring,” “call this number,”“make a call to,” and so on. The vocabulary index 744 optionallyincludes words and phrases in different languages.

Natural language processing module 732 receives the candidate textrepresentations (e.g., text string(s) or token sequence(s)) from STTprocessing module 730, and for each candidate representation, determineswhat nodes are implicated by the words in the candidate textrepresentation. In some examples, if a word or phrase in the candidatetext representation is found to be associated with one or more nodes inontology 760 (via vocabulary index 744), the word or phrase “triggers”or “activates” those nodes. Based on the quantity and/or relativeimportance of the activated nodes, natural language processing module732 selects one of the actionable intents as the task that the userintended the digital assistant to perform. In some examples, the domainthat has the most “triggered” nodes is selected. In some examples, thedomain having the highest confidence value (e.g., based on the relativeimportance of its various triggered nodes) is selected. In someexamples, the domain is selected based on a combination of the numberand the importance of the triggered nodes. In some examples, additionalfactors are considered in selecting the node as well, such as whetherthe digital assistant has previously correctly interpreted a similarrequest from a user.

User data 748 includes user-specific information, such as user-specificvocabulary, user preferences, user address, user's default and secondarylanguages, user's contact list, and other short-term or long-terminformation for each user. In some examples, natural language processingmodule 732 uses the user-specific information to supplement theinformation contained in the user input to further define the userintent. For example, for a user request “invite my friends to mybirthday party,” natural language processing module 732 is able toaccess user data 748 to determine who the “friends” are and when andwhere the “birthday party” would be held, rather than requiring the userto provide such information explicitly in his/her request.

It should be recognized that in some examples, natural languageprocessing module 732 is implemented using one or more machine learningmechanisms (e.g., neural networks). In particular, the one or moremachine learning mechanisms are configured to receive a candidate textrepresentation and contextual information associated with the candidatetext representation. Based on the candidate text representation and theassociated contextual information, the one or more machine learningmechanisms are configured to determine intent confidence scores over aset of candidate actionable intents. Natural language processing module732 can select one or more candidate actionable intents from the set ofcandidate actionable intents based on the determined intent confidencescores. In some examples, an ontology (e.g., ontology 760) is also usedto select the one or more candidate actionable intents from the set ofcandidate actionable intents.

Other details of searching an ontology based on a token string aredescribed in U.S. Utility application Ser. No. 12/341,743 for “Methodand Apparatus for Searching Using An Active Ontology,” filed Dec. 22,2008, the entire disclosure of which is incorporated herein byreference.

In some examples, once natural language processing module 732 identifiesan actionable intent (or domain) based on the user request, naturallanguage processing module 732 generates a structured query to representthe identified actionable intent. In some examples, the structured queryincludes parameters for one or more nodes within the domain for theactionable intent, and at least some of the parameters are populatedwith the specific information and requirements specified in the userrequest. For example, the user says “Make me a dinner reservation at asushi place at 7.” In this case, natural language processing module 732is able to correctly identify the actionable intent to be “restaurantreservation” based on the user input. According to the ontology, astructured query for a “restaurant reservation” domain includesparameters such as {Cuisine}, {Time}, {Date}, {Party Size}, and thelike. In some examples, based on the speech input and the text derivedfrom the speech input using STT processing module 730, natural languageprocessing module 732 generates a partial structured query for therestaurant reservation domain, where the partial structured queryincludes the parameters {Cuisine=“Sushi”} and {Time=“7 pm”}. However, inthis example, the user's utterance contains insufficient information tocomplete the structured query associated with the domain. Therefore,other necessary parameters such as {Party Size} and {Date} are notspecified in the structured query based on the information currentlyavailable. In some examples, natural language processing module 732populates some parameters of the structured query with receivedcontextual information. For example, in some examples, if the userrequested a sushi restaurant “near me,” natural language processingmodule 732 populates a {location} parameter in the structured query withGPS coordinates from the user device.

In some examples, natural language processing module 732 identifiesmultiple candidate actionable intents for each candidate textrepresentation received from STT processing module 730. Further, in someexamples, a respective structured query (partial or complete) isgenerated for each identified candidate actionable intent. Naturallanguage processing module 732 determines an intent confidence score foreach candidate actionable intent and ranks the candidate actionableintents based on the intent confidence scores. In some examples, naturallanguage processing module 732 passes the generated structured query (orqueries), including any completed parameters, to task flow processingmodule 736 (“task flow processor”). In some examples, the structuredquery (or queries) for the m-best (e.g., m highest ranked) candidateactionable intents are provided to task flow processing module 736,where m is a predetermined integer greater than zero. In some examples,the structured query (or queries) for the m-best candidate actionableintents are provided to task flow processing module 736 with thecorresponding candidate text representation(s).

Other details of inferring a user intent based on multiple candidateactionable intents determined from multiple candidate textrepresentations of a speech input are described in U.S. Utilityapplication Ser. No. 14/298,725 for “System and Method for InferringUser Intent From Speech Inputs,” filed Jun. 6, 2014, the entiredisclosure of which is incorporated herein by reference.

Task flow processing module 736 is configured to receive the structuredquery (or queries) from natural language processing module 732, completethe structured query, if necessary, and perform the actions required to“complete” the user's ultimate request. In some examples, the variousprocedures necessary to complete these tasks are provided in task flowmodels 754. In some examples, task flow models 754 include proceduresfor obtaining additional information from the user and task flows forperforming actions associated with the actionable intent.

As described above, in order to complete a structured query, task flowprocessing module 736 needs to initiate additional dialogue with theuser in order to obtain additional information, and/or disambiguatepotentially ambiguous utterances. When such interactions are necessary,task flow processing module 736 invokes dialogue flow processing module734 to engage in a dialogue with the user. In some examples, dialogueflow processing module 734 determines how (and/or when) to ask the userfor the additional information and receives and processes the userresponses. The questions are provided to and answers are received fromthe users through I/O processing module 728. In some examples, dialogueflow processing module 734 presents dialogue output to the user viaaudio and/or visual output, and receives input from the user via spokenor physical (e.g., clicking) responses. Continuing with the exampleabove, when task flow processing module 736 invokes dialogue flowprocessing module 734 to determine the “party size” and “date”information for the structured query associated with the domain“restaurant reservation,” dialogue flow processing module 734 generatesquestions such as “For how many people?” and “On which day?” to pass tothe user. Once answers are received from the user, dialogue flowprocessing module 734 then populates the structured query with themissing information, or pass the information to task flow processingmodule 736 to complete the missing information from the structuredquery.

Once task flow processing module 736 has completed the structured queryfor an actionable intent, task flow processing module 736 proceeds toperform the ultimate task associated with the actionable intent.Accordingly, task flow processing module 736 executes the steps andinstructions in the task flow model according to the specific parameterscontained in the structured query. For example, the task flow model forthe actionable intent of “restaurant reservation” includes steps andinstructions for contacting a restaurant and actually requesting areservation for a particular party size at a particular time. Forexample, using a structured query such as: {restaurant reservation,restaurant=ABC Café, date=3/12/2012, time=7 pm, party size=5}, task flowprocessing module 736 performs the steps of: (1) logging onto a serverof the ABC Café or a restaurant reservation system such as OPENTABLE®,(2) entering the date, time, and party size information in a form on thewebsite, (3) submitting the form, and (4) making a calendar entry forthe reservation in the user's calendar.

In some examples, task flow processing module 736 employs the assistanceof service processing module 738 (“service processing module”) tocomplete a task requested in the user input or to provide aninformational answer requested in the user input. For example, serviceprocessing module 738 acts on behalf of task flow processing module 736to make a phone call, set a calendar entry, invoke a map search, invokeor interact with other user applications installed on the user device,and invoke or interact with third-party services (e.g., a restaurantreservation portal, a social networking website, a banking portal,etc.). In some examples, the protocols and application programminginterfaces (API) required by each service are specified by a respectiveservice model among service models 756. Service processing module 738accesses the appropriate service model for a service and generatesrequests for the service in accordance with the protocols and APIsrequired by the service according to the service model.

For example, if a restaurant has enabled an online reservation service,the restaurant submits a service model specifying the necessaryparameters for making a reservation and the APIs for communicating thevalues of the necessary parameter to the online reservation service.When requested by task flow processing module 736, service processingmodule 738 establishes a network connection with the online reservationservice using the web address stored in the service model, and sends thenecessary parameters of the reservation (e.g., time, date, party size)to the online reservation interface in a format according to the API ofthe online reservation service.

In some examples, natural language processing module 732, dialogue flowprocessing module 734, and task flow processing module 736 are usedcollectively and iteratively to infer and define the user's intent,obtain information to further clarify and refine the user intent, andfinally generate a response (i.e., an output to the user, or thecompletion of a task) to fulfill the user's intent. The generatedresponse is a dialogue response to the speech input that at leastpartially fulfills the user's intent. Further, in some examples, thegenerated response is output as a speech output. In these examples, thegenerated response is sent to speech synthesis processing module 740(e.g., speech synthesizer) where it can be processed to synthesize thedialogue response in speech form. In yet other examples, the generatedresponse is data content relevant to satisfying a user request in thespeech input.

In examples where task flow processing module 736 receives multiplestructured queries from natural language processing module 732, taskflow processing module 736 initially processes the first structuredquery of the received structured queries to attempt to complete thefirst structured query and/or execute one or more tasks or actionsrepresented by the first structured query. In some examples, the firststructured query corresponds to the highest ranked actionable intent. Inother examples, the first structured query is selected from the receivedstructured queries based on a combination of the corresponding speechrecognition confidence scores and the corresponding intent confidencescores. In some examples, if task flow processing module 736 encountersan error during processing of the first structured query (e.g., due toan inability to determine a necessary parameter), the task flowprocessing module 736 can proceed to select and process a secondstructured query of the received structured queries that corresponds toa lower ranked actionable intent. The second structured query isselected, for example, based on the speech recognition confidence scoreof the corresponding candidate text representation, the intentconfidence score of the corresponding candidate actionable intent, amissing necessary parameter in the first structured query, or anycombination thereof.

Speech synthesis processing module 740 is configured to synthesizespeech outputs for presentation to the user. Speech synthesis processingmodule 740 synthesizes speech outputs based on text provided by thedigital assistant. For example, the generated dialogue response is inthe form of a text string. Speech synthesis processing module 740converts the text string to an audible speech output. Speech synthesisprocessing module 740 uses any appropriate speech synthesis technique inorder to generate speech outputs from text, including, but not limited,to concatenative synthesis, unit selection synthesis, diphone synthesis,domain-specific synthesis, formant synthesis, articulatory synthesis,hidden Markov model (HMM) based synthesis, and sinewave synthesis. Insome examples, speech synthesis processing module 740 is configured tosynthesize individual words based on phonemic strings corresponding tothe words. For example, a phonemic string is associated with a word inthe generated dialogue response. The phonemic string is stored inmetadata associated with the word. Speech synthesis processing module740 is configured to directly process the phonemic string in themetadata to synthesize the word in speech form.

In some examples, instead of (or in addition to) using speech synthesisprocessing module 740, speech synthesis is performed on a remote device(e.g., the server system 108), and the synthesized speech is sent to theuser device for output to the user. For example, this can occur in someimplementations where outputs for a digital assistant are generated at aserver system. And because server systems generally have more processingpower or resources than a user device, it is possible to obtain higherquality speech outputs than would be practical with client-sidesynthesis.

Additional details on digital assistants can be found in the U.S.Utility application Ser. No. 12/987,982, entitled “Intelligent AutomatedAssistant,” filed Jan. 10, 2011, and U.S. Utility application Ser. No.13/251,088, entitled “Generating and Processing Task Items ThatRepresent Tasks to Perform,” filed Sep. 30, 2011, the entire disclosuresof which are incorporated herein by reference.

4. Process for Providing Suggested User Actions in Response to DetectedAnchor

The embodiments are directed to providing, via an electronic device,suggested user actions. The suggested actions may be provided inresponse to detecting an occurrence of an anchor. An anchor may be apredefined event occurring in the user's day. For example, upon wakingup in the morning (e.g., a user waking up may be an example of ananchor), an electronic device may provide a notification that includes asuggestion for playing the newest episode of a particular podcast (i.e.,a suggested user action), where the user frequently listens to theparticular podcast upon waking up in the morning. The occurrence of theanchor may be detected via one or more signals generated by theelectronic device (e.g., the device's alarm clock function executing,the device being transitioned from a “Do Not Disturb Mode” to an “ActiveMode,” or the like). As used herein, an anchor may be a marker or signalof an event in a user's day. An occurrence of the anchor may bedetectable via the monitoring and analysis of electronic signalsgenerated by the electronic device. Based on the user's previousinteractions with the device, the occurrence of the anchor may beindicative of user behavior and/or action taken in response to the eventin the user's day. By way of non-limiting examples, an anchor mayinclude the user entering a location of interest (LOI), such as but notlimited to the user's home, office, gym, airport, shopping center, orthe like. Because an anchor may be an event that indicates userbehavior, an anchor may be referred throughout as an anchor event, orsimply an event. The occurrence of an anchor may be referred to as ananchor occurrence.

Other non-limiting examples of an occurrence of an anchor include theuser beginning to use an electronic device after an extended period ofdevice idle time, the user finishing a workout, the user waking up, theuser going to bed, the user pairing a Bluetooth-enabled device to theelectronic device, the user beginning or completing a calendar event,and the like. Still other examples of an anchor include the userlaunching a specific application and/or employing a specificfunctionality of the device. Any event detectable via signatures encodedin signals generated by the electronic device may be an anchor. Asignature (e.g., a pattern within the electronic signals generated bythe device) that indicates an occurrence of a specific anchor may bepre-determined, pre-computed, and/or pre-learned (e.g., via supervisedor unsupervised machine learning (ML) methods). Thus, the electronicdevice may monitor its various signals and detect an occurrence of thespecific anchor by identifying and/or detecting one or more signatureswithin the signals that indicates the specific anchor. For example, anoccurrence of a waking up anchor may be detected via signals of theelectronic device that indicates that the user has terminated, orotherwise ended, an “idle” state of the electronic device (or that thedevice's alarm went off). Upon detecting an occurrence of the anchor,the various embodiments may suggest one or more user actions thatcorrelate (via previously generated training data) with the occurrenceof the anchor.

A user action (i.e., an action) may include an invocation, execution, orotherwise launching a specific application, capability, functionality,or command that is enabled via the electronic device. By way ofnon-limiting examples, a user action may include but is not limited toplaying a specific audio/video content (e.g., a podcast, a musicplaylist, an audio book, a lecture, a television series, a movie, or thelike), launching an application installed on the device (e.g., a workoutapplication, a meditation application, a ride-share application, a fooddelivery application, a social network application, or the like),sending an electronic communication (e.g., email, SMS, tweet, or thelike) to another user, user group, or social network, turning on/offfunctionality of the device (e.g., turning off/on an Airplane mode ofthe device), updating various settings and/or configurations of thedevice, creating a calendar event, or the like.

When providing a suggested action, the various embodiments may provide anotification (e.g., a “pop-up” notification) that indicates thesuggested action. The notification may be interactive, in that the usermay initiate the action via an interactive selection of thenotification. Upon selecting the notification, the suggested action maybe executed and/or the execution of the action may be initiated, by thedevice. Such suggested action notifications include, but are nototherwise limited to pop-up notifications, toast notifications, passivepop-up notifications, snackbar notifications, bubble notifications, orany other such notification. In at least some embodiments, the suggestedaction may be automatically executed without the user's interactionswith the notification. For example, a user may employ a setting thatenables the automatic execution of a suggested action. As notedthroughout, the suggested action for any given anchor may not be unique.For example, multiple actions may be suggested to a user upon the userwaking up. The context of the anchor may affect the determination ofwhich actions to suggest. For example, the occurrence of a wake upanchor during a weekday may result suggesting the playing of a podcast,while the occurrence of a wake up anchor on a weekend day may result insuggesting the beginning of a workout, via a workout applicationinstalled on the device.

In the various embodiments, one or more machine learning (ML) models maybe trained to learn various statistical correlations (or associations)between occurrences of a specific anchor and actions (e.g., innovationsof device functionalities or capabilities) initiated by the user. Any ofthe various ML models employed by the various embodiments may be hereincollectively referred to as “anchor models,” because the models aretrained to detect occurrences of various anchors and suggest one or moreactions that the user is statistically likely to be initiate in responseto the occurrence (or impending occurrence) of the anchor. Thus, thevarious embodiments include the training of anchor models, as well asemploying the trained anchor models to enhance the user's experience(UX) of employing the device. That is, once deployed to the device, atrained anchor model significantly enhances the performance of thedevice because the anchor model is employed to anticipate, and suggest,one or more actions that the user is likely to take in response tovarious anchors (or events) that occur throughout the user's day.Supervised or unsupervised ML may be employed to train the models todetermine the context (e.g., contextual features) of an anchoroccurrence, and base, tailor, and/or selectively target the suggestedactions on the context of the anchor.

More specifically, the embodiments may detect, determine, and/orotherwise identify various contextual features of the anchor occurrence.The embodiments may employ the detected contextual features of theanchor occurrence, and tailor the one or more suggested actions to thecontext of the occurrence of the anchor. That is, in addition to theoccurrence of the anchor, one or more signatures within the monitoredsignals may provide contextual information (e.g., contextual features)associated with a specific occurrence of the anchor. The one or moresuggested actions that are provided may be based on the contextualinformation. Contextual features of an occurrence of an anchor (e.g.,the user waking up) may include the calendar date, the day of the week,the location of the user, the time of day that the anchor occurred, andany other such contextualizing information. Various suggested actionsmay be provided based on, and tailored to, such contextual information.For instance, if the day of the week is a weekday, or otherwise is aworkday for the user, the one or more suggested actions may include asuggestion to launch a navigation application that provides real-timeroad-traffic information and/or provides information related tocommuting to work. In contrast, if the day of the week is a weekend or anon-workday for the user, the one or more suggested actions may includedifferent and/or separate actions, such as but not limited to asuggestion to launch a workout application or play a podcast. Differentsuggested actions may be provided if the time of day that the user iswaking up is in the morning, in contrast to the suggested actionsprovided if the user is waking up from a mid-afternoon nap. In suchembodiments, the anchor models may be trained to recognize variouspatterns and/or correlations between the anchor occurrences, contextualfeatures of the anchor occurrence, and the user's response to the anchoroccurrences and the contextual features. For anchor types that arecorrelated with multiple actions, ML may be employed to train the modelsto discriminate between the multiple actions based on the context of theanchor occurrence. In some embodiments, even though more than one actiontype is positively correlated with a specific anchor type, when anoccurrence of the anchor type is detected, only the most likely of themultiple actions are suggested, based on the context of the anchoroccurrence. That is, even though multiple actions are correlated with ananchor, the anchor models may be trained to determine statisticalcorrelations between the user's likely actions and the anchoroccurrence, based on the specific contextual features of the specificanchor occurrence.

In some embodiments, the contextual information of an anchor occurrencemay include contextual differences, changes, or variances that occurafter (or before) the occurrence of the anchor. Such contextualinformation may indicate a temporal period (e.g., an amount of time)that has elapsed since the anchor occurrence, or a temporal period untilan expected anchor occurrence. In such embodiments, the moment of timeat which the electronic device provides the one or more suggestedactions may be based on contextual information, such as but not limitedto the amount of time that has passed since the occurrence of theanchor, or the amount of time till an expected anchor occurrence. Thus,the context of an anchor occurrence may be a context-based context. Insome embodiments, the context of an anchor occurrence may belocation-based context. For instance, when a user enters a location ofinterest (LOI), the context may be location-based, e.g., is the LOI theuser's home or the user's work office. In such embodiments that employtime-based context, an anchor model may be employed to determine atemporal offset, measured from the time of occurrence of the anchor (orthe expected time of an expected anchor occurrence). The one or moresuggested actions may be provided at a time based on the temporaloffset. For example, the temporal offset for providing a suggestedaction may be 15 minutes after an occurrence of the anchor associatedwith the user waking up. The suggested action (e.g., playing a podcast)may be provided at the temporal offset (e.g., 15 minutes) after theanchor occurrence. That is, the suggestion of playing the podcast may beprovided at the temporal offset, as measured after the detection of theanchor occurrence, e.g., 15 minutes after the user wakes up.

As noted above, one or more anchor models may be employed to detectanchor occurrences and provide the suggested actions. In variousembodiments, an anchor model may be trained to learn, or otherwisepredict, a likely temporal offset for providing suggested actions, aswell as other contextual features of the anchors. The temporal offsetmay be positive (e.g., provide a suggested action after an occurrence ofthe anchor and after the tolling of the temporal offset) or negative(e.g., provide a suggested action before an expected occurrence of theanchor). The above example of providing a suggestion to play a podcastafter 15 minutes upon a “wake-up” anchor is an example of a positivetemporal offset. An example of a negative temporal offset includesproviding a suggested action that includes hailing a ride, via aride-share application, three hours before a scheduled flight departurefor the user.

The detection of an anchor occurrence may trigger providing multiplesuggested actions. The multiple suggested actions may be provided atseparate temporal offsets. As a non-limiting example, a waking up anchormay be detected at 7:00 AM. The anchor may be detected via a signalgenerated when the user manually terminates (or the electronic deviceautomatically terminates based on a timer setting of a “Do Not DisturbMode”) an extended period of “idle time” of the electronic device. At afirst positive temporal offset of 5 minutes (e.g., at 7:05 AM), theelectronic device provides the user a first suggested action of playingthe most recent episode of one of the user's favorite podcast via apodcasting application installed on the device. At a second positivetemporal offset of 60 minutes (e.g., at 8:00 AM), the electronic deviceprovides the user a second suggested action of employing a weatherapplication to check the local weather report. In some embodiments,whether to provide (or not provide) the second suggested action at thesecond temporal offset may be based on whether the user initiated thefirst suggested action. For example, the second suggested action mayonly be provided if and only if (iff) the users initiated the firstsuggested action. In other embodiments, the second suggested action maybe provided at the second temporal offset, independent of whether or notthe user initiated the first suggested action. In still otherembodiments, the value of the second temporal offset may be based onwhether or not the user initiated the first suggested action. Forinstance, if the user does not listen to the suggested episode of thepodcast, the value of second temporal offset may be set to 60 minutes.If the user does listen to the podcast, the value of the second temporaloffset may be set such that the second suggested action is provided whenthe episode of the podcast is finished, or the user otherwise stopslistening to the podcast.

Continuing with this example regarding the user's day, the user maybegin their commute to work by arriving at their bus station. The busstation may be a predetermined or a “learned” (via ML) LOI and thearrival at the bus station may be a second anchor in the user's day. Theanchor occurrence may be detected via monitoring GPS signals. Thus, aLOI anchor may be detected when the user arrives at their bus station.The bus station may serve as a location-based context of the anchoroccurrence. Upon arriving at the LOI (e.g., the bus station), and basedon the location's context, a suggested action of checking when theuser's bus is arriving via a bus schedule application may be provided tothe user. As the user approaches their work office via the bus, anothersuggested action, such as but not limited to sending a message (e.g.,SMS, email, or the like) to a co-worker may automatically be provided.Providing the suggested action may be location-based context dependent.

As still another example embodiment that suggest actions based on thedetection of anchors, the user may have a flight scheduled for later inthe day. The flight may serve as an anchor, or an expected anchoroccurrence. Based on the expectation of the occurrence of the flight,one or more suggested actions may be provided, via a negative temporaloffset, before the flight. For example, the flight may be scheduled fora 7:00 PM takeoff. Based on a −1:30 temporal offset (that was “learned”during the training of an anchor model), the user may be provided with asuggested action, at 5:30 PM. The suggested action may includerequesting a rideshare service for transport to the airport, via arideshare application installed on the user's device. As the userapproaches the airport, a location-based context suggest action (e.g.,using another application) of checking into their flight may be providedto the user. Another time-based context suggest action, with anothernegative temporal offset (e.g., providing a family member a message thatthe flight is about to take off) may be provided at 6:55 PM.

The anchor models may be trained, during a training period, via trainingdata generated from the user interacting with the electronic device. Atleast a portion of the anchor-signifying signatures within the device'ssignals may be predetermined and/or pre-computed. At least a portion ofthe signatures may be indicative of contextual features of an anchoroccurrence. Such contextual signals may also be predetermined and/orpre-computed. In other embodiments, the anchor-signifying signals and/orthe contextual feature-signifying signals may be learned and/ordetermined, via training the model. For example, various deep learning(DL) methods may be employed to determine the signatures. Signatureswithin electronic signals that are indicative of an anchor occurrencemay be referred to as anchor signatures and signatures that indicatecontextual features of an anchor occurrence may be referred to ascontext signatures.

The training of an anchor model may be performed to be specific to theelectronic device and/or the specific user. For example, the training ofan anchor model may be performed using training data associated with aspecific user and/or a specific electronic device, where the trainingdata is not associated with other users and/or other devices. In someembodiments, the model may be trained using the computing resources(e.g., the processing power, storage, and/or memory) of the electronicdevice. The training data (e.g., electronic signals) may be generated bythe specific device and from device-interactions associated with thespecific user. The training data and/or the trained anchor model neednot be provided to other devices. Thus, the training of the model neednot employ computing resources, separate from the computing resources ofthe particular user's particular electronic device. Furthermore, theuser's privacy may be protected because the training data and/or thetrained model need not be provided to another electronic other than theuser's device.

In other embodiments, at least portions of the training of a model maybe off-loaded to computing resources associated with other devices. Insuch embodiments, the user's privacy may still be guarded via encryptionof the training data and/or trained model. Before being off-loaded, thetraining data generated by the user's electronic device may besufficiently encrypted. For example, an encrypted-version of the trainedmodel and/or training data may be stored via various user-accessiblecloud-computing resources. The encryption key may be stored on theuser's device, such that the trained model and/or the training data maybe inaccessible to other users and/or other devices. Thus, even thoughthe training data and/or the trained model may be “backed-up” via cloudservices, while the user's privacy is still protected.

In the various embodiments, each type of anchor (e.g., waking up,entering a LOI, end of last calendar event, finishing a workout, and thelike) may be trained separately. In some embodiments, an anchor modelmay be trained for multiple anchor types in parallel. In still otherembodiments, multiple anchor types may be trained in series. Acombination of parallel and serial training periods may be employed formultiple anchor types. For each anchor type, the training period of ananchor model may include a training data acquisition phase, as well asthree learning phases. The training data acquisition phase includesacquiring a sufficient amount of training data for the anchor type. Asexplained below, the first learning phase of the training may bereferred to as a filtering phase, the second learning phase may bereferred to as a selecting and/or ranking phase, and the third learningphase may be referred to as a temporal offsetting phase.

Acquiring training data includes acquiring and/or monitoring trainingsignals generated by the electronic device. The training signals includesignatures indicating a statistically significant number of anchoroccurrences and a statistically significant number of training actions,e.g., actions executed by the device during the data acquisition phase.The signals may also include signatures indicating contextual features,contextual conditions, or other contextual information associated withanchor occurrences. The contextual information for each anchoroccurrence may be encoded in metadata and associated with and/orincluded in the data encoding the anchor occurrence. The actions mayinclude user-initiated actions that are executed by the device, via theuser interacting with the device during the training data acquisitionphase. Each action in the training data may be classified as one or moreactions types in a set of enabled action types that are executable bythe device. Thus, the set of enabled action types may be a set ofpossible action types that are enabled and/or executable by theelectronic device. For example, the set of enabled action types mayinclude playing a podcast episode via a podcasting application, startinga workout provided by workout application, sending a message via amessenger application, requesting rideshare service via a ridesharingapplication, or the like.

After a sufficient amount of training data associated with an anchortype has been acquired via the electronic device during the dataacquisition phase, the training of the anchor model for the anchor typemay begin with the filtering phase. During the filtering phase, a set ofcandidate action types may be determined and/or generated. The set ofcandidate action types may be determined by filtering the set of enabledaction types. Because the filtering phase includes filtering the set ofenabled action types, the set of candidate action types may be a subsetof the set of enabled action types. The filtering of the set of enabledactions may include determining or generating statistical correlations,correspondences, and/or associations between the training events and theanchor occurrences of the training data. The filtering may be performedvia the statistical correlations. For example, the correlations may beemployed to determine which action types are most highly associated (orcorrelated) with the anchor type being trained. As described throughout,various methods may be employed to generate the correlations andfiltering criteria, such as but not limited to determining classconditional probabilities or posterior probabilities conditioned on theanchor type. In some embodiments, the filtering criteria may includeentropy-based filtering criteria, local maxima or minima-based filteringcriteria, or the like.

During the ranking phase, the set of candidate actions types may beranked, and at least portions of the candidate action types may beselected based on the rankings. The rankings and selection of thecandidate action types may be based the contextual conditions orcontextual features associated with the anchor occurrences. That is, theaction types that the user is most likely to initiate or execute, inresponse to (or in expectation of) the occurrence of an anchor,conditioned on particular contextual conditions or contextual featuresare highly ranked and selected for. In some embodiments, aclassification decision tree is constructed for each action type of theset of candidate action types. The nodes of a classification decisiontree for an action type may indicate one or more possible contextualconditions or contextual features of the anchor occurrences that areassociated with or conditioned on the action type and including thecorresponding contextual conditions or features. The nodes mayadditionally and/or alternatively be conditioned on the context of theaction type of the tree. For example, a node may be conditioned on thenumber of times, or frequency, that the user has initiated the actiontype in a given amount of time (e.g., the previous hour, 12 hours, day,week, or the like). To determine a probability or likelihood that theuser initiates a particular action type, conditioned on an anchoroccurrence types and on one or more contextual conditions, the nodes ofthe corresponding classification decision tree may be traversed and theprobability may be calculated based as described throughout. Thecontext-dependent classification decision trees may be employed to mapmultiple action types to a single anchor type based on the context ofanchor occurrences. The multiple action types may be differentiated fora single anchor type via the different contexts of anchor occurrences.For instance, each of a first action type (e.g., playing an episode of apodcast) and a second action type (e.g., starting a workout) may bemapped to the single anchor type of waking up. Thus, the ranking andselecting of action types may be based on the classification decisiontrees.

During the temporal offset phase of training for an anchor type, atemporal offset may be determined for one or more action types that maybe suggested in response to (or in expectation of) an occurrence of theanchor type. To determine the temporal offset, one or more temporaldistributions may be generated from the training data. The temporaldistribution may include a histogram of the temporal difference betweenanchor occurrences and user-initiated actions. In some embodiments, aseparate temporal distribution may be generated for an action type. Thetemporal offset may be determined via one or more statisticalcharacteristics of the distribution, e.g., the mean of the distribution,the median of the distribution, the mode of the distribution, a varianceof the distribution, or the like. For example, when training the modelfor the anchor type of waking up, the training data may be employed togenerate a temporal histogram for the action type of playing a podcast.The x-axis of the histogram may include bins for the temporal valuebetween the occurrence of the user waking up and the user initiatingplaying an episode of one or more podcasts. The y-axis of the histogrammay indicate the number of times (or frequency) that the user initiatesplaying a podcast at the temporal value and after the occurrence of theuser waking up. In at least some embodiments, a separate temporaldistribution may be generated for each action type and for eachcontextual condition, or combination of contextual conditions. Forinstance, separate histograms (similar to the one described above) maybe generated for wake up anchors that occurred in the morning on aweekday (or work day for the user) and wake up anchors that occurred inthe morning on a weekend day.

Once an anchor model is adequately trained for one or more anchor types,the trained model may be deployed by the electronic device. Once thetrained model is deployed, signals generated by the device may bemonitored and/or analyzed. An occurrence of an anchor type that themodel was trained for may be detected and/or identified. As noted above,in addition to indicating the anchor occurrence, the signals mayindicate one or more contextual conditions of the anchor occurrence. Thecontextual conditions may be encoded in metadata associated with theanchor occurrence. In response to and/or in accordance with the detectedanchor occurrence, the model may be employed to determine and provideone or more suggested actions and a temporal offset for each of the oneor more suggested actions. The one or more suggested actions, as well asthe associated temporal offsets, may be based on the contextualconditions of the anchor occurrence. At the temporal offset forsuggested action, as measured from the anchor occurrence, the electronicdevice may provide the user with a notification indicating the suggestedaction. The user may be enabled to select the notification, and uponselection by the user, the electronic device may execute the selectedsuggested action. As discussed throughout, the model may be employed todetect anchor occurrences that are expected to occur in the future(e.g., a flight later in the day that, for which, the user is scheduledto take). Via a negative temporal offset, a suggested action (e.g.,requesting a rideshare to the airport) may be provided to the user priorto the scheduled flight.

In a non-limiting embodiment, an anchor type associated with the usermay be trained after a sufficient amount of training data has beenacquired via the user interacting the device upon waking up. Thetraining data may include signals generated by the device. A portion ofthe signals may encode multiple occurrences of one or more anchor typesand another portion of the signals may encode actions initiated by theuser. Anchor occurrences encoded in the training data may be referred toas training anchor occurrences and actions encoded in the training datamay be referred to as training actions. The user may have initiated atleast a portion of the training actions in response to training anchoroccurrences. The signals may additionally encode metadata indicatingcontextual conditions of at least some of the anchor occurrences. Eachof the training actions may be classified as one or more of the actiontypes that are enabled via the electronic device. More specifically,each training action may be classified as one of the action types in theset of enabled action types.

In some embodiments, an anchor model may be trained for multiple anchortypes in parallel. In other embodiments, the model may be trained foranchor types in a serial progression. After a sufficient amount oftraining data associated with a particular anchor type has beencollected, the filtering phase for that particular anchor type maybegin. In the various embodiments, a sufficient amount of training datafor a particular anchor type may include an amount of training data thatencodes a statistically significant number of anchor occurrences of theparticular anchor type and a statistically significant numberco-occurring actions of one or more action types. As used herein, astatistically significant number of anchor occurrences and/or actionsmay refer to a sufficient number of anchor occurrences and/or actions,such that one or more statistical metrics (e.g., error bars, confidencelevels, or the like) may be quantified to within one or more thresholds.Note that when associating multiple actions to a single anchor type,where the multiple actions are differentiated via the contextualconditions of the anchor occurrence, a larger volume of training datamay be required.

During the filtering phase, various candidate action types arestatistically correlated with the particular anchor type being trained,based on anchor occurrences and user-initiated actions encoded in thetraining data (e.g., co-occurrences of anchors and actions within thetraining data). More specifically, a large, strong, or high correlationbetween an anchor type and a particular action type indicate frequentco-occurrences, within the training data, of anchors and actions thatare classified as the particular action type. A small, low, or weakcorrelation between an anchor type and a particular action type indicateinfrequent, or non-existent, co-occurrences, within the training data,of anchor and actions that are classified as the particular action type.As discussed throughout, the co-occurrences in the training data neednot be temporally simultaneous, but may be separated by temporal ranges,characterized via one or more temporal offsets. The correlations may beconditioned on the contextual conditions or features of anchoroccurrences. The correlations are employed to filter the electronicdevice's set of enabled actions types to generate or determine a set ofcandidate action types. Various methodologies may be employed todetermine the correlations between the anchor types and action types.For example, various supervised or unsupervised machine learning (ML)methods may be employed to identify patterns (e.g., co-occurrences ofanchors and actions) within the signals of the training data.

In the following discussion, the exemplary but non-limiting anchor typeof waking up, is employed to discuss the training of an anchor type.However, this discussion is non-limiting, and the discussion may beapplicable to any number of other anchor types. Once a sufficient amountof training data associated with the waking up anchor type, whichencodes a statistically significant number of occurrences of the userwaking up and a statistically significant number of actions initiatedupon the user waking up, the filtering phase of training the model forthe waking up anchor type may be performed. The filtering phase includesgenerating, determining, and/or identifying a set of candidate actiontypes that is a subset of the set of enabled actions. In variousembodiments, correlations between the anchor occurrences and the actiontypes are determined via the training data. More specifically, astatistical correlation between the anchor type (e.g., waking up) andeach enabled action type may be determined via the training data. Such astatistical correlation between the anchor type and a particular actiontype may indicate a frequency of co-occurrence of an occurrence of theanchor and the user initiating the particular action type. Thecorrelations may be determined via one or more probability distributionsextractable from the training data. For example, one or more classconditional probability distributions and/or one or more posteriorprobability distributions may be extracted from the training data. Aprobability distributions may be conditions on the co-occurrence of theanchor and one or more actions. The correlations may be determined viathe one or more probability distributions.

Note that the co-occurrence of anchors and actions need not besimultaneous, but may be separated by a range of temporal values. Asdiscussed below, the third learning phase (i.e., the temporal offsetphase) of the training is directed towards determining a temporal offsetbased on the range of temporal values between an anchor occurrence andthe initiation and/or execution of an action. For example, if the userfrequently plays a particular podcast shortly after waking up (e.g.,typically between 10 minutes and 20 minutes), a relatively large orstrong correlation between the anchor and the action type (e.g., playingthe particular podcast) is determined. If the user rarely (or never)views a bus schedule shortly after waking up, a relatively small or weak(or zero) correlation between the anchor and the action type oflaunching a bus schedule application is determined. Enabled action typesthat are more strongly correlated with the anchor type (e.g., playing apodcast) are included in the set of candidate action types. Enabledaction types that are less correlated with the anchor type (e.g.,launching the bus schedule application) are not included in the set ofcandidate action types. In some embodiments, the set of enabled actiontypes is filtered to determine the set of candidate actions types.Enabled action types that “pass” through the filter are included in theset of candidate action types.

As noted above, various supervised or unsupervised ML methods may beemployed to determine the correlations. In general, ML methods may beemployed to identify patterns of anchor and event co-occurrences in thetraining data that is associated with the anchor type. The correlationsthat are employed to identify the set of candidate action types may bedetermined based on the patterns (e.g., co-occurrences) of anchors andevents in the training data. One non-limiting example of an unsupervisedML method applicable to the various embodiments is associated rulelearning. In some embodiments, the correlations may be generated byapplying one or more association rule learning methods and/oralgorithms, such as but not limited to apriori learning algorithm, onthe training data. In general, an association rule learning method isemployed to analyze co-occurrences of the anchor and action types withinthe training data. Apriori rule learning, which is directed towardslearning association rules from co-occurring events (e.g., anchors andactions) is one such algorithm that may be applied to the training datato determine the correlation between anchor occurrences and actiontypes.

In other embodiments, a neural network (NN) architecture that implementsan attention mechanism may be employed to determine the correlationsbetween anchor occurrences and action types, within the training data.When implemented within a NN, attention mechanism increase the abilityof the NN to discover and/or learn patterns within training data. Ingeneral, the analysis implemented by the NN is directed to portions oftraining data, where the patterns are more likely to be discovered. Thatis, the “attention” of the NN is directed towards more appropriateportions of the training data, rather than applying the same amount ofattention to all portions of the training data. For example, withattention applied towards “long term patterns”, patterns associated with“long term” dependencies within the training data may be more readilydiscovered than with NN that don't implement attention mechanisms.Accordingly, when attention mechanisms are employed, the co-occurrencesof anchors and actions may be more readily discovered. In still otherembodiments, other methods that analyze patterns within data may beemployed to determine the correlations between anchor occurrences anduser actions. In still other embodiments, a SparseMax algorithm may beemployed applied to the training data. That is, a deep learning NN maybe employed to identify patterns in the training data, where anactivation function of at least one of the layers of the NN is aSparseMax function. A SparseMax activation function has the generalproperty that the output vector is a relatively sparse vector. Thus, thestrongest correlations are identified in the training data. In someembodiments, a canonical correlation analysis (CCA) may be employed todetermine the correlations in the training data.

The filtering of the set of enabled actions types to generate the set ofcandidate action types may be based on a threshold applied to thecorrelation determined for each action type. In other embodiments, thefiltering may be a based on a relative and/or absolute number of actiontypes to be include in the set of candidate action types. In onenon-limiting example, the filtering phase of the training of the modelfor the waking up anchor type may filter the set of enabled actiontypes, such that the set of candidate action types includes five actiontypes: playing a podcast, playing a song in the user's music library,viewing recent photos via a photo application, checking the weatherforecast via a weather application, or playing a game installed on thedevice. Upon the identification of the set of candidate actions types,via the filtering phase, the ranking phase of the training period maybegin.

During the ranking phase, the set of candidate actions types may beranked, and at least portions of the candidate action types may beselected based on the rankings. The rankings and selection of thecandidate action types may be based the contextual conditions orcontextual features associated with the anchor occurrences encoded inthe training data. That is, the action types, of the action types in theset of candidate action types, that the user is most likely to initiateor execute, in response to (or in expectation of) the occurrence of ananchor are ranked highly and selected for in the ranking phase. Theselected action types may be conditioned on particular contextualconditions or contextual features of the anchor occurrences. In someembodiments, a ranking for a particular action type of the set ofcandidate action types may be determined based on training actions thatare classified as the particular action type. The ranking of theparticular action type may be further based on the contextual conditionindicated by metadata associated with the training anchor occurrencesthat are correlated with the training actions that are classified as theparticular action type.

In the ranking phase, a decision tree learning method may be employed tolearn to distinguish to distinguish amongst the contextual conditions ofanchor occurrences and determine a suggested action based on thecontextual conditions. For instance, a classification decision treealgorithm may be employed, such as but not limited iterativedichotomiser 3 (ID3) algorithm may be applied to the relevant portion ofthe training data to generate a classification decision tree for eachaction type (or each of the highest ranked action types) of the set ofcandidate action types. A classification decision tree for a particularaction type indicates which contextual conditions of the anchoroccurrence to associate with the action type, and which contextualconditions to not form an association with the anchor occurrence. Inthis way, the anchor model is trained to distinguish amongst thecontextual conditions of the anchor occurrence and tailor the one ormore suggested actions to the anchor occurrence based on the contextualconditions. The nodes indicate one or more contextual conditions andwhether the one or more contextual conditions “classify” the anchoroccurrence (with the contextual conditions) as being associated with theaction type.

FIG. 8A illustrates a classification decision tree 800 for the actiontype of playing a particular podcast and the anchor type of the userwakening up, according to various examples. Classification decision tree800 was generated via an application of a classification decision treealgorithm on the training data. It is understood that classificationdecision tree 800 is an exemplary embodiment only, and the embodimentsmay vary. A classification decision tree generally subdivides thetraining data into smaller and smaller subsets of training data. Eachsubset is conditioned on the contextual conditions associated with thenodes. Each node indicates whether the node (and thus the associatedsubset of training data) is in accordance with the contextual conditionsencountered while traversing the tree. The upper node 802 indicates thatthe classification decision tree 800 is for the classification of “playa particular podcast.” Tree 800 is traversed, starting root node 804, bytraversing the contextual conditions associated with an anchoroccurrence. In this discussion, node 804 is referred to as the root nodebecause it indicates the first “decision” in the tree.

In a classification tree (e.g., tree 800), the root node (e.g., node804) is typically conditioned on the contextual condition that is moststrongly predictive of the class (e.g., playing the particular podcast).In tree 800, root node 804 is conditioned on whether the anchor (e.g.,waking up) occurred on a weekday or weekend. Root node indicates thatthe training data included 44 samples of the anchor occurrences, andthat 23 of the 44 samples occurred on a weekday and the remaining 13samples occurred on a weekend (e.g., values=[31, 13]). Nodes in decisiontrees may be characterized by a Gini metric, Gini coefficient, Giniindex, which indicates the inequality of the underlying distribution, asconditioned on the contextual condition of the node. In someembodiments, the Gini metric may vary between 0.0 and 1.0 (or someotherwise normalized range). Root node 804 is associated with a Ginimetric of 0.499. If all the anchor occurrences happened on a weekday,the Gini metric may be closer to 1.0, while if all the anchoroccurrences happened on a weekend, the Gini metric would be closer to0.0.

Starting from the root node 804, if the anchor condition occurred on aweekday, then traversing flows to node 806. If the anchor occurrenceoccurred on a weekend, then traversing flows to node 808. Node 806 isconditioned on the contextual condition of playing the podcast at least8 times in the previous 10 days of the anchor occurrence. Node 808 isconditioned on the contextual condition of the anchor occurrenceoccurring on a Saturday, rather than a Sunday. Nodes 806 and 808 providethe sample numbers, values of the sample numbers, and Gini metricsassociated with the distribution. Nodes 810, 812, and 814 are leaf nodesand indicate the various distributions, sample numbers of thedistribution, and associated Gini metrics. The class indicator at eachnode indicates whether an anchor occurrence with the associatedcontextual conditions should be classified as the class (e.g., play theparticular podcast). Tree 800 indicates that the action type of playingthe particular podcast should be suggested when the user wakes up on aweekday and if the user has been playing the podcast frequently in thelast 10 days (e.g., greater than 7 times in the last 10 days).

FIG. 8B illustrates a classification decision tree 820 for the actiontype of playing a particular song and the anchor type of the userwakening up, according to various examples. Tree 820 is similar to tree800 of FIG. 8A, except the tree 820 was generated for the action type ofplaying a particular song from the user's music library upon waking up.Tree 820 indicates that a suggested action of playing the particularsong when the user has listened to the song (upon waking up) greaterthan 6 times in the last 10 days, but less than 23 times in the last 30days. Thus, the embodiments may provide the suggested action of playingthe particular song when the user has listened to the song relativelyfrequently in the last 10 days, and the user has not listened to it toofrequently in the last 30 days. The classification decision treealgorithm may be employed to generate such a classification decisiontree for each action type in the set of candidate action types. In someembodiments, as indicated by tree 800 and tree 820, if the contextualconditions of the anchor occurrence are such that the anchor occurrencemay be classified as both playing the particular podcast and playing theparticular song, both action types may be suggested to the user. Notethat each action type may have a separate temporal offset. Thus, thesuggested action types may be provided at separate temporal offsets. Forexample, if the temporal offset for playing the podcast is 30 minutesand the temporal offset for playing the particular song is 15 minutes,the embodiments may suggest playing the song 15 minutes and waking upand suggest playing the podcast 30 minutes after waking up.

As indicated above, the temporal offset phase of the training isdirected towards determining a temporal offset for the suggested actiontypes. In some embodiments, one or more of the candidate action typesmay be selected for determining the temporal offset for the candidateaction type. In some embodiments, selecting the action types may bebased on the ranking of the candidate action types and/or theclassification decision trees generated for the candidate action types.The portion of the training data that is associated with the selectedaction type is employed to generate a temporal distribution of the timewhen the user initiated the selected action type, in response to theanchor occurrence. The temporal distribution for that action type may beemployed to determine the temporal offset for that action type. As notedthroughout, the temporal offset for an action type may be employed todetermine the point of time (after or before) the occurrence of ananchor that the suggested action is provided to the user.

In various embodiments, a histogram may be generated of the temporaldistribution generated for the selected candidate action type. Thetemporal offset may be determined via the histogram. FIG. 9 illustratesa histogram 900 for the temporal distribution of the action type ofplaying a particular podcast, in conjunction with a waking up anchor,according to various examples. Histogram 900 is provided only as anexample, and the embodiments may vary in the construction of histogramsfor the various action types. Histogram 900 was generated from thetraining data associated with the anchor type waking up and trainingactions (e.g., the user initiating the playing of the podcast) that theuser initiated in response to the anchor occurrence. The x-axisindicates the time (in seconds) between the anchor occurrence and theuser initiating playing the particular podcast. In various embodiments,the x-axis is subdivide into a plurality of bins. The y-axis indicatesthe number of times (within the training data) that the action type wasinitiated by the user within the time indicated by the x-axis value ofthe bin. Thus, the y-axis value of the various bins indicate thetemporal distribution for the action type and anchor occurrence. Thetemporal offset may be determined via a statistical metric of thetemporal distribution. For example, in some non-limiting embodiments,the temporal offset for playing the particular podcast may be determinedvia at least one of the mean or median of the temporal distributionshown in histogram 900. At seen in FIG. 9, most of the times that theuser initiated playing the podcast, upon waking up, were within 700seconds. Thus, in this non-limiting example, the temporal offset for theaction type of playing the particular podcast, upon waking up, may beset to approximately 10 minutes.

Although not shown, another temporal distribution (and histogram) may begenerated for the action type of playing a particular song may begenerated and a separate temporal offset may be determined. In someembodiments, if enough statistics are included in the training data, aseparate temporal distribution and/or histogram may be generated foreach of the contextual conditions of the anchor type. For example,separate histograms may be generated for anchor occurrences where theuser wakes up on a weekday and anchor occurrences where the user wakesup on a weekend.

Upon completion of the filtering, ranking, and temporal offset phases ofthe training, the anchor model may be updated to provide suggestedactions, as learned via the training. Once updated, the anchor model maybe deployed to the device to begin providing suggested actions, inresponse (or anticipation) of anchor occurrences. In variousembodiments, once deployed at the electronic device, the model may beiteratively updated, periodically or from time to time, based on userfeedback. That is, the model may be iteratively updated, based on theuser's engagement with the suggested actions. When the user positivelyengages with a particular suggested action (e.g., the user selects theparticular suggested action to initiate its execution), the model isupdated to reinforce or increase the probability that the model providesthe suggested action in response to future anchor occurrences. When theuser does not engage (or negatively engages) with the particularsuggested action (e.g., the user does not select the particularsuggested action to initiate its execution), the model is updated todecrease the probability that the model provides the particularsuggested action in response to future anchor occurrences. In someembodiments, one or more reinforcement learning methods may be employedto iteratively update the anchor models, based on user engagement withthe suggested actions or other user feedback. The reward functionemployed in such reinforcement learning embodiments may be greater whenthe user selects suggested actions, than when the user does not selectsuggested actions. Thus, the anchor models may be iteratively updatedbased on the user's response to the suggested actions.

FIG. 10 illustrates process 1000 for training a predictive model thatenables providing suggested actions in response to an occurrence of ananchor event, according to various examples. Process 1000 is performed,for example, using one or more electronic devices implementing a digitalassistant. In some examples, process 1000 is performed using aclient-server system (e.g., system 100), and the blocks of process 1000are divided up in any manner between the server (e.g., DA server 106)and a client device. In other examples, the blocks of process 1000 aredivided up between the server and multiple client devices (e.g., amobile phone and a smart watch). Thus, while portions of process 1000are described herein as being performed by particular devices of aclient-server system, it will be appreciated that process 1000 is not solimited. In other examples, process 1000 is performed using only aclient device (e.g., user device 104) or only multiple client devices.In process 1000, some blocks are, optionally, combined, the order ofsome blocks is, optionally, changed, and some blocks are, optionally,omitted. In some examples, additional steps may be performed incombination with the process 1000. The operations described above withreference to FIG. 10 are optionally implemented by components depictedin FIGS. 1-4, 6A-6B, and 7A-7C. It would be clear to a person havingordinary skill in the art how other processes are implemented based onthe components depicted in FIGS. 1-4, 6A-6B, and 7A-7C.

Process 1000 begins at block 1002, where training data is acquired fortraining a predictive model, such as but not limited to an anchor model.Once trained, the anchor model is employed to provide suggested actions,in response to (or in anticipation of) the occurrence of an anchorand/or anchor event. The anchor may be an event that indicates one ormore behaviors of a user of an electronic device. Accordingly, in theforegoing discussion, the anchor may be referred to as an event, and anoccurrence of the anchor may be referred to as an event occurrence. Thetraining data includes signals generated by the electronic device thatenables a set of enabled actions. The signals may be referred to astraining signals. At block 1004, the signals may be analyzed to identifyevent occurrences, associated contextual conditions, and actions,initiated by the user of the device, in response to (or in anticipationof) the event occurrences. The event occurrences, the associatedcontextual conditions, and the training actions may be encoded in thesignals. The contextual conditions may be included in metadataassociated with the event occurrences. Each of the training actions maybe classified as one or more of the action types of the set of enabledactions.

At block 1006, statistical correlations between the event occurrencesand the action types based on the training signals. In some embodiments,the correlations may be determined between the event occurrences and atleast a portion of the training actions. The statistical correlationsmay be determined via any of the various embodiments discussed herein.For example, the correlations may be determined via one or moresupervised or unsupervised machine learning (ML) methods and/oralgorithms. At block 1008, a set of candidate action types may bedetermined based on the correlations between the event occurrences andthe action types. The set of candidate action types may be a subset ofthe set of enabled actions. In some embodiments, the set of enabledaction types may be filtered to generate and/or identify the set ofcandidate actions. Any of the various filters and/or filtering methodsdiscussed herein may be employed to filter the set of enabled actiontypes. In some embodiments, blocks 1006 and 1008 may include thefiltering phase of training the predictive model.

At block 1010, a ranking for each of the action types of the set ofcandidate action types may be determined and/or generated. The rankingsmay be based on a portion of the training actions that are classified asthe action type being ranked, the contextual conditions of the eventoccurrences correlated with the portion of the training actions, and thecorrelations between the portion of the training actions and theassociated event occurrences. At block 1012, one or more actions typesmay be selected from the set of candidate action types. The selection ofthe action types may be based on the rankings of the candidate actiontypes. In various embodiments, blocks 1010 and 1012 may include theranking phase of training the predictive model. Thus, at blocks 1010and/or 1012, one or more classification decision trees, as discussedthroughout, may be generated for each of the ranked action types. Theclassification decision trees may be based on the contextual conditionsof the associated event occurrences. The selection of the action typesmay be based on the classification decision trees. Non-limiting examplesof such classification decision trees are discussed in conjunction withat least FIGS. 8A-8B.

At block 1014, one or more temporal distributions may be generated foreach action type selected at block 1012. The temporal distribution for aparticular action type may indicate a temporal difference between eventoccurrences and associated training actions classified as the selectedaction type. A separate temporal distribution may be generated for eachaction type selected at block 1012. A separate temporal distribution maybe generated for each of the different contextual conditions of theevent occurrences. A histogram may be generated for each of the temporaldistributions. A non-limiting example of a histogram is discussed inconjunction with at least FIG. 9. At block 1016, a temporal offset isdetermined for each of the one or more action types selected at block1012. The temporal offset may be determined based on the temporaldistribution and/or histogram generated for the selected action type. Invarious embodiments, blocks 1014 and 1016 may include the temporaloffset phase of training the predictive model.

At block 1018, the predictive model may be updated, in accordance to thetraining of process 1000. In some embodiments, the predictive model maybe updated to provide the user, at the electronic device and in responseto the occurrence of another event, one or more suggested actions inaccordance to the event type of the other event occurrence, one or moreof the selected action types, the one or more temporal offsets for theone or more selected action types, and the contextual conditions of theother event occurrence. That is, the trained event or anchor model maybe deployed at the electronic device. Various embodiments for deployinga trained predictive model on an electronic device are described in inconjunction with at least FIG. 11.

FIG. 11 illustrates process 1100 for deploying a trained predictivemodel at an electronic device, according to various examples. Similar toprocess 1000, process 1100 may be performed, for example, using one ormore electronic devices implementing a digital assistant. Process 11100may be performed by any device, and/or combination of devices, similarto the devices and/or combination of devices that process 1000 may beperformed by.

Process 1100 starts, at block 1102, where an event (or anchor)occurrence, as well as contextual conditions, are detected viamonitoring and analysis of signals generated by the electronic device.At block 1104, a trained predictive model is employed to determine oneor more suggested actions and one or more temporal offsets based on thedetected event occurrence and the associated contextual conditions. Insome embodiments, the one or more suggested actions and the one or moreassociated temporal offsets are received, via the predictive model, atthe electronic device. In some embodiments, the predictive model may betrained by one or more embodiments described at least in conjunctionwith FIG. 10. At block 1106, the electronic device is employed toprovide, to the user, the one or more suggested actions, in accordancewith the associated one or more temporal offsets. At block 1108, an inresponse to receiving an indication and/or selection of a providedsuggested action, initiate the execution of the selected suggestedaction, via the electronic device.

In various embodiments, once deployed at the electronic device, themodel may be iteratively updated, periodically or from time to time,based on user feedback. That is, the model may be iteratively updated,based on the user's engagement with the suggested actions. Suchiteratively updating is not shown in FIG. 11. However, it is understoodthat process 1100 may be modified to include iteratively updating thepredictive model based on user engagement and/or feedback in response tothe suggested actions. When the user positively engages with aparticular suggested action (e.g., the user selects the particularsuggested action to initiate its execution), the model is updated toreinforce or increase the probability that the model provides thesuggested action in response to future anchor occurrences. When the userdoes not engage (or negatively engages) with the particular suggestedaction (e.g., the user does not select the particular suggested actionto initiate its execution), the model is updated to decrease theprobability that the model provides the particular suggested action inresponse to future anchor occurrences. In some embodiments, one or morereinforcement learning methods may be employed to iteratively update theanchor models, based on user engagement with the suggested actions orother user feedback. The reward function employed in such reinforcementlearning embodiments may be greater when the user selects suggestedactions, than when the user does not select suggested actions. Thus, theanchor models may be iteratively updated based on the user's response tothe suggested actions.

In accordance with some implementations, a computer-readable storagemedium (e.g., a non-transitory computer readable storage medium) isprovided, the computer-readable storage medium storing one or moreprograms for execution by one or more processors of an electronicdevice, the one or more programs including instructions for performingany of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises means forperforming any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises a processing unitconfigured to perform any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises one or moreprocessors and memory storing one or more programs for execution by theone or more processors, the one or more programs including instructionsfor performing any of the methods or processes described herein.

Example methods are disclosed herein. One example method may be fortraining a predictive model (e.g., and anchor model) for an anchor (orevent) that indicates a behavior user of an electronic device enabled toexecute each action type of a set of enabled action types. Theelectronic device may have one or more processors and a memory. Theelectronic device may perform one or more actions and or operations. Themethod may include, at the electronic device and based on a plurality ofsignals generated by the electronic device, detecting a plurality oftraining actions and a plurality of event occurrences of the event. Eachof the plurality of event occurrences may be associated with metadataindicating a contextual condition of the event occurrence. Each of theplurality of training actions may be initiated via the user interactingwith the electronic device and is classified as an action type of theset of enabled action types. A set of candidate action types may bedetermined. Determining the set of candidate action types may be basedon a plurality of correlations between each of the plurality of trainingactions and each of the plurality of event occurrences. The set ofcandidate action types may be a subset of the set of enabled actiontypes. A ranking for each action type of the set of candidate actiontypes may be determined. Determining the ranking for an action type maybe based on a portion of the plurality of training actions that areclassified as the action type. Determining the ranking of the actiontype may be further based on the one or more contextual conditionsindicated by the metadata associated with a portion of the plurality ofevents occurrences that are correlated, via the plurality ofcorrelations, with the portion of the plurality of training actions. Insome embodiments, a first action type of the set of candidate actiontypes may be selected based on the determined ranking for each actiontype of the set of candidate action types. A first portion of theplurality of training actions and a first portion of the plurality ofevent occurrences may be selected. Each of the first portion of theplurality of training actions may be classified as the first actiontype. Each of the first portion of the plurality of event occurrencesmay be correlated, via the plurality of correlations, with at least oneof the first portion of the plurality of training actions. A temporaloffset for the first action type may be determined based on a temporaldistribution of the first of the plurality of training actions, withrespect to the first portion of the plurality of event occurrences. Insome embodiments, the predictive model may be updated to generate, inresponse to another occurrence of the event, a suggested action. Theprovided suggested action is in accordance with the first action typeand the temporal offset of the first action type.

The example method may include employing an association rule learningalgorithm to determine the plurality of correlations between each of theplurality of training actions and each of the plurality of eventoccurrences. In some embodiments, a canonical correlation analysis maybe employed to determine the plurality of correlations between each ofthe plurality of training actions and each of the plurality of eventoccurrences. In still other embodiments, attention within a neuralnetwork may be employed to determine the plurality of correlationsbetween each of the plurality of training actions and each of theplurality of event occurrences. In or more other embodiments, aSparseMax algorithm may be employed to determine the plurality ofcorrelations between each of the plurality of training actions and eachof the plurality of event occurrences.

Determining the set of candidate action types may include applying afilter on the set of enabled action types and the filter is based on theplurality of correlations between each of the plurality of trainingactions and each of the plurality of event occurrences. The filter maybe an entropy filter. In other embodiments, the filter is aclass-conditional probability filter. In still other embodiments, thefilter is one of a local maxima filter or a local minima filter. In someembodiments, the filter may include filtering on the number of timesthat the candidate action type has been initiated in response to (or inexpectation of) the occurrence of the anchor in a previous amount oftime (e.g., how many times the user has initiated the candidate actionin the previous 28 days). In other embodiments, the filter may filter onthe posterior probability associated with the candidate action type. Instill other embodiments, the filter may filter on the class conditionalprobability associated with the candidate action type (e.g., probabilityof the anchor conditioned on the candidate action type). The filter maybe a combination of multiple filters. For instance, the filter may be acombination of a filter on the number of times (or frequency) that theuser has initiated the candidate action type in response to the anchor,a filter on the posterior probability of the candidate action type, anda filter on the class conditional probability of the anchor conditionedon the candidate action type.

The example method may further include, for each action type of the setof candidate action types, generating a classification decision tree.The nodes of the classification decision tree may be conditioned oncontextual conditions associated with the portion of the plurality ofevents occurrences that are correlated with the portion of the pluralityof training actions that are classified as the action type. Theclassification decision tree may classify other event occurrences withthe contextual conditions as associated with the action type. In someembodiments, selecting the first action type of the set of candidateaction types may be further based on the classification decision treefor the first action type. The classification decision tree for eachaction type of the set of candidate action types may be employed toassociate a plurality of action types of the set of candidate actiontypes with the event. The plurality of action types may bedifferentiated by the contextual conditions of the plurality of eventoccurrences.

The example method may further include generating a first histogram forthe first action type based on the temporal distribution of the first ofthe plurality of training actions. The temporal offset for the firstaction type may be determined based on the first histogram. The methodmay further include generating a second histogram for the first actiontype based on a second temporal distribution of the plurality oftraining actions. The temporal distribution may be condition on a firstset of contextual conditions and the second temporal distribution may beconditioned on a second set of contextual conditions. A second temporaloffset for the first action type may be determined based on the secondtemporal distribution of the plurality of training actions. Thepredictive model may be updated to generate, in response to a firstoccurrence of the event that is condition on the first set of contextualconditions, the suggested action, in accordance with the first actiontype and the temporal offset of the first action type. The predictivemodel may be further updated to generate, in response to a secondoccurrence of the event that is condition on the second set ofcontextual conditions, a second suggested action, in accordance with thefirst action type and the second temporal offset of the first actiontype.

The temporal offset for the first action type may be further based on atleast one of a mean or a median of the temporal distribution of theplurality of training actions. The predictive model may be stored and/orencrypted on the electronic device. The temporal offset may be less thanzero or greater than zero. Generating the suggested action may includeproviding a notification, at the electronic device, at a time that isseparated from the other event occurrence by the temporal offset.

Another example method may be for employing a predictive model for anevent that indicates a behavior of a user of an electronic device. Theother method may include, based on one or more signals generated by theelectronic device, detecting an event occurrence of the event. The eventoccurrence may be associated with metadata indicating a contextualcondition of the event occurrence. In accordance with the eventoccurrence, a suggested action and a temporal offset may be receivedfrom the predictive model. The suggested action may be provided to theuser within the temporal offset from the event occurrence.

In the method, the event may include a termination of a do not disturbmode of the electronic device and the behavior of the user includes theuser waking up. Detecting the event occurrence may include determining,based on a positioning signal of the one or more signals, that theelectronic device is positioned at a particular location. The particularlocation may include at least one of an airport, a bus terminal, or atrain terminal. In other embodiments, the particular location includesat least one of a residence of the user, a workplace of the user, arecreational location, or a fitness center. The event occurrence mayinclude an occurrence of a calendar event of an electronic calendarassociated with the user. Providing the suggested action may includeproviding the suggested action after the event occurrence, and inaccordance to the temporal offset. In other embodiments, providing thesuggested action includes providing the suggested action before theevent occurrence, and in accordance to the temporal offset. In stillother embodiments, providing the suggested action includes employing adisplay of the electronic device to providing a pop-up notificationindicating the suggested action.

The method may further include receiving a user selection, at theelectronic device, of the suggested action. The electronic device may beemployed to automatically execute the suggested action. The predictivemodel may be trained based on a plurality of training signals generatedby the electronic device. One or more machine learning (ML) methods maybe employed to train the predictive model, via the training signals. Insome embodiments, the method further includes, in accordance with thepredictive model, providing a second suggested action to the user withina second temporal offset from the event occurrence. The suggested actionmay be based on at least an event type of the event occurrence. Thesuggested action may be further based on at least one or more contextualconditions of the event occurrence. The one or more contextualconditions of the event occurrence may include at least one of a day ofa week, a time of a day, or a location of the electronic device. Thesuggested action may include at least one of playing content on theelectronic device, launching an application installed on the electronicdevice, or sending a message to a first contact within a plurality ofcontacts for the user. The application installed on the electronicdevice may include at least one of a rideshare application, a physicalfitness application, or a food delivery application.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the techniques and their practical applications. Othersskilled in the art are thereby enabled to best utilize the techniquesand various embodiments with various modifications as are suited to theparticular use contemplated.

Although the disclosure and examples have been fully described withreference to the accompanying drawings, it is to be noted that variouschanges and modifications will become apparent to those skilled in theart. Such changes and modifications are to be understood as beingincluded within the scope of the disclosure and examples as defined bythe claims.

As described above, one aspect of the present technology is thegathering and use of data available from various sources providesuggested actions in response detecting events and/or anchors. Thepresent disclosure contemplates that in some instances, this gathereddata may include personal information data that uniquely identifies orcan be used to contact or locate a specific person. Such personalinformation data can include demographic data, location-based data,telephone numbers, email addresses, twitter IDs, home addresses, data orrecords relating to a user's health or level of fitness (e.g., vitalsigns measurements, medication information, exercise information), dateof birth, or any other identifying or personal information.

The present disclosure recognizes that the use of such personalinformation data, in the present technology, can be used to the benefitof users. For example, the personal information data can be used toprovide suggested actions in response to detected events and/or anchorsto the user. Accordingly, use of such personal information data enablesusers to calculated control of the delivered content. Further, otheruses for personal information data that benefit the user are alsocontemplated by the present disclosure. For instance, health and fitnessdata may be used to provide insights into a user's general wellness, ormay be used as positive feedback to individuals using technology topursue wellness goals.

The present disclosure contemplates that the entities responsible forthe collection, analysis, disclosure, transfer, storage, or other use ofsuch personal information data will comply with well-established privacypolicies and/or privacy practices. In particular, such entities shouldimplement and consistently use privacy policies and practices that aregenerally recognized as meeting or exceeding industry or governmentalrequirements for maintaining personal information data private andsecure. Such policies should be easily accessible by users, and shouldbe updated as the collection and/or use of data changes. Personalinformation from users should be collected for legitimate and reasonableuses of the entity and not shared or sold outside of those legitimateuses. Further, such collection/sharing should occur after receiving theinformed consent of the users. Additionally, such entities shouldconsider taking any needed steps for safeguarding and securing access tosuch personal information data and ensuring that others with access tothe personal information data adhere to their privacy policies andprocedures. Further, such entities can subject themselves to evaluationby third parties to certify their adherence to widely accepted privacypolicies and practices. In addition, policies and practices should beadapted for the particular types of personal information data beingcollected and/or accessed and adapted to applicable laws and standards,including jurisdiction-specific considerations. For instance, in the US,collection of or access to certain health data may be governed byfederal and/or state laws, such as the Health Insurance Portability andAccountability Act (HIPAA); whereas health data in other countries maybe subject to other regulations and policies and should be handledaccordingly. Hence different privacy practices should be maintained fordifferent personal data types in each country.

Despite the foregoing, the present disclosure also contemplatesembodiments in which users selectively block the use of, or access to,personal information data. That is, the present disclosure contemplatesthat hardware and/or software elements can be provided to prevent orblock access to such personal information data. For example, in the caseof providing suggested actions, the present technology can be configuredto allow users to select to “opt in” or “opt out” of participation inthe collection of personal information data during registration forservices or anytime thereafter. In another example, users can select notto provide training data and/or trained models to other devices and/orother users. In yet another example, users can select to limit thelength of time mood-associated data is maintained or entirely prohibitthe development of a baseline mood profile. In addition to providing“opt in” and “opt out” options, the present disclosure contemplatesproviding notifications relating to the access or use of personalinformation. For instance, a user may be notified upon downloading anapp that their personal information data will be accessed and thenreminded again just before personal information data is accessed by theapp.

Moreover, it is the intent of the present disclosure that personalinformation data should be managed and handled in a way to minimizerisks of unintentional or unauthorized access or use. Risk can beminimized by limiting the collection of data and deleting data once itis no longer needed. In addition, and when applicable, including incertain health related applications, data de-identification can be usedto protect a user's privacy. De-identification may be facilitated, whenappropriate, by removing specific identifiers (e.g., date of birth,etc.), controlling the amount or specificity of data stored (e.g.,collecting location data at a city level rather than at an addresslevel), controlling how data is stored (e.g., aggregating data acrossusers), and/or other methods.

Therefore, although the present disclosure broadly covers use ofpersonal information data to implement one or more various disclosedembodiments, the present disclosure also contemplates that the variousembodiments can also be implemented without the need for accessing suchpersonal information data.

What is claimed is:
 1. A method for employing a predictive model for anevent that indicates a behavior of a user of an electronic device, themethod comprising: employing one or more processors and a memory of theelectronic device to perform operations comprising: based on one or moresignals generated by the electronic device, detecting an eventoccurrence of the event, wherein the event occurrence is associated withmetadata indicating a contextual condition of the event occurrence; inaccordance with the event occurrence, receiving a suggested action and atemporal offset from the predictive model; and providing the suggestedaction to the user within the temporal offset from the event occurrence.2. The method of claim 1, wherein the event includes a termination of ado not disturb mode of the electronic device and the behavior of theuser includes the user waking up.
 3. The method of claim 1, whereindetecting the event occurrence includes determining, based on apositioning signal of the one or more signals, that the electronicdevice is positioned at a particular location.
 4. The method of claim 3,wherein the particular location includes at least one of an airport, abus terminal, or a train terminal.
 5. The method of claim 3, wherein theparticular location includes at least one of a residence of the user, aworkplace of the user, a recreational location, or a fitness center. 6.The method of claim 1, wherein the event occurrence includes anoccurrence of a calendar event of an electronic calendar associated withthe user.
 7. The method of claim 1, wherein providing the suggestedaction includes providing the suggested action after the eventoccurrence, and in accordance to the temporal offset.
 8. The method ofclaim 1, wherein providing the suggested action includes providing thesuggested action before the event occurrence, and in accordance to thetemporal offset.
 9. The method of claim 1, wherein providing thesuggested action includes employing a display of the electronic deviceto providing a pop-up notification indicating the suggested action. 10.The method of claim 1, further comprising: receiving a user selection,at the electronic device, of the suggested action, and employing theelectronic device to execute the suggested action.
 11. The method ofclaim 1, wherein the predictive model was trained based on a pluralityof training signals generated by the electronic device.
 12. The methodof claim 1, wherein one or more machine learning (ML) methods wereemployed to train the predictive model.
 13. The method of claim 1,further comprising: in accordance with the predictive model, providing asecond suggested action to the user within a second temporal offset fromthe event occurrence.
 14. The method of claim 1, wherein the suggestedaction is based on at least an event type of the event occurrence. 15.The method of claim 1, wherein the suggested action is based on at leastone or more contextual conditions of the event occurrence.
 16. Themethod of claim 15, wherein the one or more contextual conditions of theevent occurrence include at least one of a day of a week, a time of aday, or a location of the electronic device.
 17. The method of claim 1,wherein the suggested action includes at least one of playing content onthe electronic device, launching an application installed on theelectronic device, or sending a message to a first contact within aplurality of contacts for the user.
 18. The method of claim 17, whereinthe application installed on the electronic device includes at least oneof a rideshare application, a physical fitness application, or a fooddelivery application.
 19. An electronic device, comprising: one or moreprocessors; a memory storing one or more programs, the one or moreprograms including instructions, which when executed by the one or moreprocessors of the electronic device, cause the electronic device to:based on one or more signals generated by the electronic device, detectan event occurrence of the event, wherein the event occurrence isassociated with metadata indicating a contextual condition of the eventoccurrence; in accordance with the event occurrence, receive a suggestedaction and a temporal offset from a predictive model; and provide thesuggested action to a user within the temporal offset from the eventoccurrence.
 20. A non-transitory computer-readable storage mediumcomprising one or more programs for execution by one or more processorsof an electronic device, the one or more programs including instructionswhich, when executed by the one or more processors, cause the electronicdevice to: based on one or more signals generated by the electronicdevice, detect an event occurrence of the event, wherein the eventoccurrence is associated with metadata indicating a contextual conditionof the event occurrence; in accordance with the event occurrence,receive a suggested action and a temporal offset from a predictivemodel; and provide the suggested action to a user within the temporaloffset from the event occurrence.